Vehicle loan generation system: prequalified vehicle loan offer generation

ABSTRACT

The system and method allows a vehicle loan organization to generate multiple, customized vehicle loan offers to an applicant for different types of vehicles in an automated fashion. The system and method calculates an acquisition score to better determine the riskiness of offering a vehicle loan to a potential applicant. Additionally, the system and method automates underwriting decisions by automatically approving, denying, or referring vehicle loan applications. For vehicle loan applications that are referred for manual underwriting, the system determines the appropriate credit analyst to use based on the application&#39;s complexity, the analyst&#39;s expertise, and the analyst&#39;s availability. Further, the system uses credit data to calculate a maximum term, amount, and LTV ratio for potential vehicle loans. Also, the system considers the applicant&#39;s collateral before approving a vehicle loan. For qualified, approved applicants, the system generates multiple, customized vehicle loan offers for the applicant.

RELATED APPLICATIONS

This application claims benefit to the filing date of U.S. ProvisionalPatent Application 61/877,883, filed Sep. 13, 2013, the contents ofwhich are expressly incorporated herein by reference.

FIELD OF TECHNOLOGY

The present disclosure generally relates to a system and a method forgenerating vehicle loans and, more particularly, to a system that cangather and analyze data to generate multiple, customized vehicle loanoffers for an applicant.

BACKGROUND

Organizations providing vehicle loans gather and analyze various typesof data before offering vehicle loans. The gathered and analyzed dataassists the organization in choosing which applicants to offer loans,and what loans to offer the chosen applicants. However, this process canbe challenging for an organization because of the numerous tasks andrisks associated with offering vehicle loans to applicants. The risk ofan applicant defaulting on a credit loan depends on the applicant. Thus,organizations need to avoid risky applicants. Also, the conditions ofthe loan, such as amount and length, alter the riskiness of the loan forthe organization. Further, the conditions of the loan, such as theamount, length, and interest rate, must be desirable to the applicant.As a result, offering desirable loans to applicants who will not defaultis a challenge for organizations providing vehicle loans.

SUMMARY OF THE INVENTION

A computer implemented method for generating prequalification vehicleloan offers for one or more applicants including receiving one or morevehicle loan applications including vehicle loan information from one ormore applicants, requesting credit data associated with the applicantsfrom one or more credit bureaus, receiving the credit data associatedwith the applicants from the credit bureaus, the credit data including aset of attributes for each applicant, identifying, by one or morecomputer processors, potential customers for prequalification from theone or more applicants, applying, by the one or more computerprocessors, a front end criteria to the identified potential customersto exclude one or more identified potential customers, selecting, by theone or more processors, an applicant from the remaining one or moreidentified potential customers, determining, by the one or more computerprocessors, an estimated vehicle collateral value for a vehicle for theselected applicant based on the set of attributes and/or vehicle loaninformation for the selected applicant, determining, by the one or morecomputer processors, a maximum prequalified vehicle loan amount for avehicle loan for the vehicle for the selected applicant based on the setof attributes and/or vehicle loan information for the selectedapplicant, generating, by the one or more computer processors, acustomized prequalification offer for the selected applicant byincluding the estimated vehicle collateral value and the maximumprequalified vehicle loan amount, and sending the prequalification offerto the selected applicant.

In another embodiment, a computer system for generating prequalificationvehicle loan offers for one or more applicants including one or morecomputer processors and a program memory storing executable instructionsthat when executed by the one or more computer processors cause thecomputer system to receive one or more vehicle loan applicationsincluding vehicle loan information from one or more applicants, requestcredit data associated with the applicants from one or more creditbureaus, receive the credit data associated with the applicants from thecredit bureaus, the credit data including a set of attributes for eachapplicant, identify, by the one or more computer processors, potentialcustomers for prequalification from the one or more applicants, apply,with the one or more computer processors, a front end criteria to theidentified potential customers to exclude one or more identifiedpotential customers, select an applicant from the remaining one or moreidentified potential customers, determine, with the one or more computerprocessors, an estimated vehicle collateral value for a vehicle for theselected applicant based on the set of attributes and/or vehicle loaninformation for the selected applicant, determine, with the one or morecomputer processors, a maximum prequalified vehicle loan amount for avehicle loan for the vehicle for the selected applicant based on the setof attributes and/or vehicle loan information for the selectedapplicant, generate, with the one or more computer processors, acustomized prequalification offer for the selected applicant byincluding the estimated vehicle collateral value and the maximumprequalified vehicle loan amount, and send the prequalification offer tothe selected applicant.

In yet another embodiment, a non-transitory computer-readable storagemedium including computer-readable instructions to be executed on one ormore processors of a system for generating prequalification vehicle loanoffers for one or more applicants, the instructions when executedcausing the one or more processors to receive one or more vehicle loanapplications including vehicle loan information from one or moreapplicants, request credit data associated with the applicants from oneor more credit bureaus, receive the credit data associated with theapplicants from the credit bureaus, the credit data including a set ofattributes for each applicant, identify, by the one or more computerprocessors, potential customers for prequalification from the one ormore applicants, apply, with the one or more computer processors, afront end criteria to the identified potential customers to exclude oneor more identified potential customers, select an applicant from theremaining one or more identified potential customers, determine, withthe one or more computer processors, an estimated vehicle collateralvalue for a vehicle for the selected applicant based on the set ofattributes and/or vehicle loan information for the selected applicant,determine, with the one or more computer processors, a maximumprequalified vehicle loan amount for a vehicle loan for the vehicle forthe selected applicant based on the set of attributes and/or vehicleloan information for the selected applicant, generate, with the one ormore computer processors, a customized prequalification offer for theselected applicant by including the estimated vehicle collateral valueand the maximum prequalified vehicle loan amount, and send theprequalification offer to the selected applicant.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed herein. It should be understood that each figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

FIG. 1 is a diagram of an example of an organization generating vehicleloans for a customer, according to one embodiment.

FIG. 2 is a diagram of an example of a vehicle loan generation system,according to one embodiment.

FIG. 3 is a block diagram of an example of a vehicle loan generationsystem implementation, according to one embodiment.

FIG. 4 is a diagram of an example of an acquisition scoring modelenvironment, according to one embodiment.

FIG. 5A is a diagram of an example of an acquisition scoring modelinputs table, according to one embodiment.

FIG. 5B is a diagram of an example of a keycoding table, according toone embodiment.

FIG. 6 is a diagram of an example of an acquisition scoring modelcontribution table, according to one embodiment.

FIG. 7 is a diagram of an example of an automated underwriting modelenvironment, according to one embodiment.

FIG. 8 is a diagram of an example of an automated underwriting dualmatrix, according to one embodiment.

FIG. 9A is a diagram of an example of an automobile negative outcomedual matrix, according to one embodiment.

FIG. 9B is a diagram of an example of a recreational vehicle and boatsnegative outcome dual matrix, according to one embodiment.

FIG. 10 is a diagram of an example of a dual matrix split regiondecision tree, according to one embodiment.

FIG. 11 is a diagram of an example of an automated underwriting modelbusiness rules table, according to one embodiment.

FIG. 12 is a diagram of an example of a credit limit assignment modelenvironment, according to one embodiment.

FIG. 13A is a diagram of an example of a term determination modelenvironment, according to one embodiment.

FIG. 13B is a diagram of an example of a vehicle maximum term chart,according to one embodiment.

FIG. 14A is a diagram of an example of an automobile risk segmentationenvironment for the term determination model, according to oneembodiment.

FIG. 14B is a diagram of an example of a recreational vehicle and boatrisk segmentation environment for the term determination model,according to one embodiment.

FIG. 14C is a diagram of an example of an “other product” risksegmentation environment for the term determination model, according toone embodiment.

FIG. 15A is a diagram of an example of a new car low risk segment graphfor the term determination model, according to one embodiment.

FIG. 15B is a diagram of an example of a new car medium risk segmentgraph for the term determination model, according to one embodiment.

FIG. 15C is a diagram of an example of a new car high risk segment graphfor the term determination model, according to one embodiment.

FIG. 16 is a diagram of an example of the policy guidelines for the termdetermination model, according to one embodiment.

FIG. 17 is a diagram of an example of a loan to value (LTV) cut offdetermination model environment, according to one embodiment.

FIG. 18A is a diagram of an example of a vehicle loans LTV cut offtable, according to one embodiment.

FIG. 18B is a diagram of an example of a vehicle loans LTV averagecollateral value table, according to one embodiment.

FIG. 18C is a diagram of an example of a vehicle loans LTV averagecharge off amount table, according to one embodiment.

FIG. 19A is a diagram of an example of a used car risk segmentationenvironment for the LTV cut off model, according to one embodiment.

FIG. 19B is a diagram of an example of a new car risk segmentationenvironment for the LTV cut off model, according to one embodiment.

FIG. 19C is a diagram of an example of a recreational vehicle and boatrisk segmentation environment for the LTV cut off model, according toone embodiment.

FIG. 19D is a diagram of an example of an “other products” risksegmentation environment for the LTV cut off model, according to oneembodiment.

FIG. 20A is a diagram of an example of a used car, low risk segmentgraph environment for the LTV cut off model, according to oneembodiment.

FIG. 20B is a diagram of an example of a used car, medium risk segmentgraph environment for the LTV cut off model, according to oneembodiment.

FIG. 20C is a diagram of an example of a used car, high risk segmentgraph environment for the LTV cut off model, according to oneembodiment.

FIG. 21 is a block diagram of an example of a payment capacity modelenvironment for the LTV cut off model, according to one embodiment.

FIG. 22 is a diagram of an example of a maximum payment capacitycalculation environment, according to one embodiment.

FIG. 23 is a diagram of an example of a maximum payment capacitycalculation environment, according to another embodiment.

FIG. 24 is a block diagram of a prequalification model environment forthe prequalification model, according to one embodiment.

FIG. 25 is a flow diagram of an example method for generating aprequalification offer for an applicant and processing the subsequentresponse, according to one embodiment.

FIG. 26 is an example diagram of a prequalification offer, according toone embodiment.

FIG. 27A is an example diagram of front end criteria used forprequalification, according to one embodiment.

FIG. 27B is a diagram of fatal criteria used for prequalification,according to one embodiment.

FIG. 28 is a block diagram of a payment capacity estimator environment,according to one embodiment.

FIG. 29 is a block diagram of a collateral estimation environment,according to one embodiment.

FIG. 30A is a diagram of a collateral segmentation table, according toone embodiment.

FIG. 30B is a flow diagram of a method for assigning a value tocollateral, according to one embodiment.

FIG. 30C is a diagram of a low collateral estimator table, according toone embodiment.

FIG. 30D is a diagram of a high collateral estimator table, according toone embodiment.

FIG. 31A is a diagram of a premium collateral probability estimator,according to one embodiment.

FIG. 31B is a diagram of a high collateral probability estimator,according to one embodiment.

FIG. 31C is a diagram of a low collateral probability estimator,according to one embodiment.

FIG. 31D is a diagram of a key coded estimator variable table, accordingto one embodiment.

FIG. 32 is a block diagram of a multiple offers model environment 3200,according to one embodiment.

FIG. 33 is a diagram of a snapshot of a product type inputs interface,according to one embodiment.

FIG. 34 is a diagram of a snapshot of a multiple offers model inputsinterface, according to one embodiment.

FIG. 35 is a block diagram of an offer generation model environment,according to one embodiment.

FIG. 36 is a block diagram of a policy guidelines environment, accordingto one embodiment.

FIG. 37 is a diagram of policy guidelines tables, according to oneembodiment.

FIG. 38 is a diagram of a maximum term policy guidelines table,according to one embodiment.

FIG. 39 is a block diagram of a pricing model environment, according toone embodiment.

FIG. 40 is a diagram of a pricing constraints table, according to oneembodiment.

FIG. 41 is a block diagram of an annual interest rate determinationenvironment 4100, according to one embodiment.

FIG. 42 is an example diagram of an automobile annual interest ratedetermination environment, according to one embodiment.

FIG. 43 is an example of an automobile annual interest ratedetermination, according to one embodiment.

FIG. 44 is an example diagram of a recreational vehicles/boats annualinterest rate determination environment, according to one embodiment.

FIG. 45 is an example of a recreational vehicle annual interest ratedetermination, according to one embodiment.

FIG. 46 is a block diagram of a loan amount calculation engineenvironment, according to one embodiment.

FIG. 47 is a screenshot of multiple personalized vehicle loan offersgenerated for an applicant, according to one embodiment.

FIG. 48 is a block diagram of the offer customization model environmentaccording to one embodiment.

FIG. 49 is a screenshot of an offer customization interface and agenerated customized offer for an applicant, according to oneembodiment.

FIG. 50 is a screenshot of a selected vehicle loan offer, according toone embodiment.

FIG. 51 is a block diagram of the skill based routing model environment,according to one embodiment.

FIG. 52 is a block diagram of a loan complexity model environment,according to one embodiment.

FIG. 53 is a diagram of a loan processing time environment, according toone embodiment.

FIG. 54 is a diagram of a loan complexity segmentation environment,according to one embodiment.

FIG. 55 is a block diagram of a loan allocation engine environment,according to one embodiment.

FIG. 56 is a diagram of an analyst eligibility table, according to oneembodiment.

FIG. 57 is a diagram of a loan complexity table, according to oneembodiment.

FIG. 58 is a diagram of a vehicle loan analyst prioritization table,according to one embodiment.

FIG. 59 is a diagram of a fair allocation limits environment, accordingto one embodiment.

FIG. 60 is a diagram of a vehicle loan analyst tiers environment,according to one embodiment.

DETAILED DESCRIPTION OF THE DRAWINGS

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention is defined by the words of the claims set forthat the end of this patent. The detailed description is to be construedas exemplary only and does not describe every possible embodiment, asdescribing every possible embodiment would be impractical, if notimpossible. One could implement numerous alternate embodiments, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘_(——————)’ ishereby defined to mean . . . ” or a similar sentence, there is no intentto limit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based on any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this patent isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only so as to not confuse the reader,and it is not intended that such claim term be limited, by implicationor otherwise, to that single meaning. Finally, unless a claim element isdefined by reciting the word “means” and a function without the recitalof any structure, it is not intended that the scope of any claim elementbe interpreted based on the application of 35 U.S.C. § 112, sixthparagraph.

In today's environment, vehicle loan organizations, such as lendersproviding vehicle loans, seek to achieve current and future financialgoals and objectives by improving their vehicle loan products, servicesand processes. Lenders can improve their processes by automating variousdecisions, determinations, and calculations. Lenders can further improvetheir processes by reducing the risk they assume when offering vehicleloans. Also, lenders can improve their services by generating multiplevehicle loan offers for a potential applicant. Furthermore, customizedvehicle loan offers for an applicant would improve vehicle loan productsprovided by lenders to the applicant. This in turn would increase thechances of the applicant choosing the lender's product, as opposed tothat of a competitor.

Lenders strive to reduce the risk of default when offering vehicleloans. This risk varies based on an applicant's credit worthiness.Additionally, metrics such as the loan term, amount, and loan to value(LTV) ratio can also affect the risk of default. Processes aimed atbetter determining an applicant's credit worthiness and appropriatemaximum loan terms (i.e., loan length), amounts, and LTV ratios reducethe risk taken by lenders.

Vehicle loan processes can also be enhanced by automation. Thus, theautomatic determination of credit worthiness, maximum loan terms,amounts, and LTV ratios improve processes by reducing risk andincreasing efficiency. Moreover, underwriting decisions can be furtherautomated by relying on an applicant's calculated credit worthiness.Thus, processes are improved by increased automation of underwritingdecisions and metrics calculations.

The metrics calculations also drive improvements to vehicle loanproducts and services. For example, multiple vehicle loan offers can begenerated to comply with the maximum loan terms, amounts, and LTV ratioscalculated. By providing multiple vehicle loan offers to an applicant,an applicant has the flexibility to choose the vehicle loan product thatbest suits him, thereby improving the likelihood of the applicantselecting the organization's vehicle loan products.

Additionally, a lender can boost their prospects with the applicant bycustomizing the multiple vehicle loan offers. Currently, a tool ormethod implementing these improvements for vehicle loan products,services, and processes does not exist. The vehicle loan generationsystem addresses this issue.

Vehicle Loan Generation System Overview

FIG. 1 is a diagram of a vehicle loan generation environment 100,according to an embodiment. In the displayed embodiment, vehicle loangeneration environment 100 includes an organization 101 which engageswith applicant 150. For environment 100, organization 101 gathers andreceives data from applicant 150. The organization 101 provides vehicleloan products and/or services to applicant 150. Also, organization 101may generate one or more vehicle loans for applicant 150. Alternatively,organization 101 may choose to not offer a vehicle loan to applicant 150due to a high risk of default.

The organization 101 may include one or more people, such as employees,partners, members, owners, directors, officers, shareholders, or otherconstituents of an organization 101. The organization 101 may be a legalentity, such as a partnership, corporation, sole proprietorship, or alimited liability company. In some cases, the organization 101 is abusiness. Also, the organization 101 may include one or moredepartments, divisions, entities, sectors, units, businesses, etc. Insome embodiments, the organization 101 is a vehicle financing company.The products and services of the organization 101 may include providingvehicle loans to an applicant 150. The loans may be for a variety ofvehicles, such as automobiles, cars, trucks, recreational vehicles,boats, motorcycles, scooters, and/or other vehicles. The organization101 may provide multiple vehicle offers to an applicant 150 for a singlevehicle. Furthermore, applicant 150 may request vehicle loan offers formultiple vehicles from organization 101.

In FIG. 1, the organization 101 includes a server 102. The server 102may be used to implement the vehicle loan generation system for theorganization 101. The vehicle loan generation system may receive,format, organize, store, process, update, modify, and/or analyze dataabout one or more applicants 150. The vehicle loan generation system maythen be used by organization 101 to determine whether or not to makevehicle loan offers to applicant 150. Additionally, the vehicle loangeneration system may be used to generate multiple offers fororganization 101 to an applicant 150 for a vehicle. Further,organization 101 may use the vehicle loan generation system to provideapplicant 150 vehicle loans for one or more vehicles.

In the displayed embodiment in FIG. 1, the server 102 is located at theorganization 101. Alternatively, the server 102 may be remotely located,that is, the server 102 is not located at the organization 101. Theserver 102 may be hosted by an entity other than organization 101. Theserver 102 may include more than one server. In this case, the multipleservers 102 may work together to provide a platform that supports thevehicle loan generation system for the organization 101.

The server 102 may include a database 103. Database 103 may be used tostore data received from one or more applicants 150. In FIG. 1, thedatabase 103 is located on the server 102. However, the database 103could be remotely located, i.e., the database 103 is not located on theserver 102. Additionally, the database 103 may be located on one or moreservers 102. FIG. 3 contains more details about the server 102 anddatabase 103.

In FIG. 1, applicants 150 may engage the organization 101 by requestinga vehicle loan. Alternatively, the organization 101 may contact theapplicant 150 to inquire about the applicant's vehicle financing needs.Additionally, applicant 150 may already be a customer of organization101 by already having a vehicle loan with organization 101. In thiscase, applicant 150 may be in the market for another vehicle loan for adifferent vehicle. Alternatively, applicant 150 may choose to engageorganization 101 to refinance his vehicle. When organization 101 engageswith applicant 150, organization 101 may request (154) data fromapplicant 150. In response, applicant 150 may send data (152) toorganization 101.

The data exchange (152, 154) between the organization 101 and applicant150 may enhance the vehicle loan process for both organization 101 andapplicant 150. Specifically, organization 101 can determine whetherapplicant 150 is too risky for a vehicle loan. Additionally,organization 101 can assess maximum amounts, time periods, interestrates, and/or other vehicle loan terms that are appropriate forapplicant 150. Furthermore, organization 101 can use the exchanged datato better customize vehicle loan offers for applicant 150.

Applicants 150 may be one or more persons, a legal entity, business,and/or organization. In some instances, the applicants are an employeeor agent of a legal entity, business, and/or organization. For example,the applicant 150 could be a person, John Doe, looking for a vehicleloan for his automobile. Alternatively, the applicant 150 could be amarried couple, John Doe and Jane Doe, looking for a vehicle loan fortheir recreational vehicle. In another embodiment, the applicant 150could be a company, such as a rental car company, looking for a vehicleloan for an automobile.

FIG. 1 also shows the organization 101 communicating with credit bureau160. Organization 101 may request credit bureau 160 to provide creditdata (164). In some embodiments, the requested credit data (164) isabout applicant 150. Credit bureau 160 may oblige the request fromorganization 101 and send some or all of the requested data (162).

Additionally, FIG. 1 shows organization 101 communicating withunderwriting organization 170. Organization 101 may send a request (174)to underwriting organization 170 to analyze a vehicle loan applicationfrom applicant 150 and determine if the underwriting organization 170will underwrite the loan. The underwriting organization 170 may respond(172) to the request from organization 101. The response (172) fromunderwriting organization 170 may be that it will underwrite the vehicleloan for applicant 150. Alternatively, the response (172) may be todecline underwriting the vehicle loan for applicant 150. Alternatively,the response (172) may be to approve the applicant 150 for underwritingby a different underwriting organization.

In some embodiments, the organization 101 may communicate with more,fewer, and/or different entities than those displayed in FIG. 1. In someembodiments, the organization 101 may exchange more, less, and/ordifferent communications, data, requests, and/or responses than thoseshown in FIG. 1. FIG. 3 provides additional details about the dataexchange between organization 101 and other entities, such as applicant150, credit bureau 160, and underwriting organization 170.

FIG. 2 is a diagram of an example vehicle loan generation system 200.The system 200 may be provided and/or used by the organization 101 togenerate vehicle loans for an applicant 150, in some embodiments. Thevehicle loan generation system 200 has several models, such as theacquisition scoring model 210, the automated underwriting model 220, thecredit limit assignment model 230, the prequalification model 240, themultiple offers model 250, and the skill based routing model 260. Someor all of the models may work together to generate vehicle loans for anapplicant 150. In some embodiments, the vehicle loan generation system200 includes fewer, more, and/or different models than those displayedin FIG. 2. The vehicle loan generation system 200 can also be used togenerate loans for various vehicles, including automobiles, cars,trucks, recreational vehicles, boats, motorcycles, scooters, and/orother vehicles.

The acquisition scoring model 210 is used by the vehicle loan generationsystem 200 to calculate an acquisition score for an applicant 150. Theacquisition score is meant to evaluate the credit worthiness of theapplicant 150. The acquisition score is calculated based on varioustypes of credit data, some or all of which are associated with theapplicant 150. By doing this, the acquisition scoring model 210 providesthe vehicle loan generation system with an enhanced measure of anapplicant's credit worthiness. In one embodiment, the acquisitionscoring model 210 relies on a set of model variables to determine theacquisition score of an applicant 150. This reduces the risk assumed bythe organization 101 for the vehicle loans generated by the system 200.

The automated underwriting model 220 relies on the acquisition scoregenerated by acquisition scoring model 210 to further automateunderwriting. The automated underwriting model 220 determines if thevehicle loan applicant is automatically denied, automatically approved,or referred for manual underwriting. The automated underwriting model220 makes this determination by using applicant credit data, such as aFICO score, in conjunction with the aforementioned acquisition score. Inone embodiment, the automated underwriting model 220 uses a FICO customdual matrix to make this determination. The matrix includes regions forautomatically approving, automatically declining, or referring anapplicant based on their acquisition score and their FICO score. Theautomated underwriting model 220 may also rely on additional bureauattributes to make automatic approve or decline determinations. Thevehicle loan generation system 200 is enhanced by the automatedunderwriting model 220 because manual underwriting is reduced, which maybe a timely process. This reduction improves turnaround time of thevehicle application.

The credit limit assignment model 230 calculates the maximum term,amounts, and loan to value (LTV) ratios that are appropriate for anapplicant 150. The credit limit assignment model 230 uses a termdetermination model, a loan to value (LTV) cut off determination model,and a payment capacity model to determine these metrics. The creditlimit assignment model makes these determinations based on vehicleinformation, loan performance information, and various credit data,including credit data associated with the applicant 150. Thesedeterminations are used by other models of the vehicle loan generationsystem to generate loans that are less risky for the organization 101.

The prequalification model 240 evaluates whether an applicant isqualified to receive a vehicle loan based on the value of his collateraland his payment capacity. The prequalification model 240 relies on acollateral estimation model and a payment capacity estimator to makethese determinations. Additionally, the prequalification model 240 alsouses the maximum term, amounts, and LTV ratios determined by the creditlimit assignment model 230, along with vehicle information, loanperformance information, and credit data. Evaluation of whether anapplicant is qualified to receive a loan based on the aforementioneddata further reduces the risk assumed by the organization when offeringa vehicle loan to applicant 150.

The multiple offers model 250 generates multiple offers for an applicant150. Additionally, the multiple offers model 250 customizes offers foran applicant 150. When generating multiple offers, the multiple offersmodel 250 relies on previously calculated metrics, such as maximum term,amounts, and LTV ratios. Based on these previously calculated values,additional credit data, and various business rules, the multiple offersmodel 250 generates multiple offers for an applicant 150. The vehicleloan offers will differ with regards to the term, the monthly payments,the loan amount, and/or the interest rate. By generating multiple offersfor the vehicle loan generation system 200, an applicant 150 is able tochoose the vehicle loan offer that best suits him.

The skill based routing model 260 is called upon when the automatedunderwriting model 220 determines that a vehicle loan applicationrequires manual underwriting. The skill based routing model attempts toimprove the assignment of referred vehicle loan applications to creditanalysts. Skill based routing model 260 uses a loan complexity model anda loan allocation engine to accomplish this goal. The loan complexitymodel categorizes vehicle loans into complexity groups based on theexpected loan processing time. Meanwhile, the loan allocation engineassigns applications to credit analysts based on the analyst'sexpertise, availability, and the determined loan complexity. Byimproving the assignment of the referred applications, the skill basedrouting model 260 empowers the vehicle loan generation system 200 toturn around vehicle loan applications that require manual underwritingfaster. In some embodiments, the credit analysts are part of theorganization, 101. In other embodiments, the credit analysts are part ofa different underwriting organization 170.

Vehicle Loan Generation System Implementation

FIG. 3 is a block diagram of a vehicle loan generation systemimplementation 300, according to one embodiment. The system 300 may beimplemented by server 102 in communication over network 316 with otherdevices. Server 102 may communicate over network 316 with applicantcomputing device 320. Additionally, the server 102 may be accessed overthe network 316 by user-interface 312B and organization computing device318. Furthermore, the server 102 may communicate over the network 316with an underwriting organization computing device 324 and/or a creditbureau computing device 322. In some embodiments, fewer or more devicescommunicate with server 102 over network 316, while in otherembodiments, different devices communicate with server 102 over network316.

As mentioned earlier in FIG. 1, server 102 may be, for example, acomputer, a server, a plurality of networked computing devices having alogical appearance of a single computing device, a plurality of cloudcomputing devices, etc. Accordingly, for ease of discussion only and notfor limitation purposes, the server 102 is referred to herein using thesingular tense, although in some embodiments the server 102 may includemore than one physical computing device.

The server 102 may include a memory 307, a processor 301 (may be calleda controller, a microcontroller, or a microprocessor), a random-accessmemory (RAM) 303, and an input/output (I/O) circuit 315, all of whichmay be interconnected via an address/data bus 305. The memory 307 maycomprise one or more tangible, non-transitory computer-readable storagemedia or devices, and may be configured to store computer-readableinstructions that, when executed by the processor 301, cause the server102 to implement the vehicle loan generation system 300.

Memory 307 may store computer-readable instructions and organize theminto modules that can be executed to implement the vehicle loangeneration system 300. In the displayed embodiment, memory 307 storesvehicle loan generation module 312, text mining module 311, automatedunderwriting module 302, prequalification module 304, credit limitassignment module 306, acquisition scoring module 308, multiple offersmodule 309, and skill based routing module 310. In some embodiments, thememory 307 may store different modules than those displayed, while inother embodiments, the memory 307 may store fewer or more modules thanthose displayed. In some embodiments, the executable computer-readableinstructions may not be organized as modules. In some embodiments,instructions may be organized as routines, subroutines, or other blocksof instructions.

Vehicle loan generation module 312 includes instructions executed byprocessor 301 to generate loans for applicant 150. The vehicle loangeneration module 312 may first call the acquisition scoring module 308.The acquisition scoring module 308 includes instructions executed byprocessor 301 to generate an acquisition score for an applicant 150. Theacquisition scoring module 308 may request credit data about applicant150 in order to generate the acquisition score. The requested data maybe retrieved from database 103. Alternatively, the requested credit datamay be retrieved over network 316 from credit bureau computing device322. Once the credit data for the applicant 150 is received, acquisitionscoring module 308 uses the retrieved credit data to generate theacquisition score. The acquisition score may then be stored in thedatabase 103 for the applicant 150.

Next, vehicle loan generation module 312 calls automated underwritingmodule 302. Automated underwriting module 302 includes instructionsexecuted by processor 301 to automate the underwriting decision process.Specifically, the automated underwriting module will determine whetherthe vehicle loan application from applicant 150 should be automaticallyapproved, automatically denied, or referred for manual underwriting.Automated underwriting module 302 may access database 103 to retrievecredit data about applicant 150 and the acquisition score generated byacquisition scoring module 308 for applicant 150. The retrieved creditdata and acquisition score enable the automated underwriting module 302to then determine if applicant 150 should be automatically approved,denied, or referred for manual underwriting.

After this, vehicle loan generation module 312 may call credit limitassignment module 306. Credit limit assignment module 306 includesinstructions executed by processor 301 to determine a maximum amount,maximum term, a maximum loan to value (LTV) ratio, and a maximum monthlypayment for vehicle loans for applicant 150. The credit limit assignmentmodule 306 uses various types of credit data to make this determination.Similar to the acquisition scoring module 308, this credit data may beretrieved from database 103 and/or credit bureau computing device 322via network 316. With this data, the credit limit assignment module 306determines the maximum amount, term, and LTV for vehicle loans forapplicant 150.

After this, the vehicle loan generation module 312 may call multipleoffers module 309. Multiple offers module 309 includes instructionsexecuted by processor 301 to generate multiple offers that comply withthe maximum term, maximum amount, and maximum LTV ratio determined bycredit limit assignment module 306. Additionally, multiple offers module309 customizes the multiple offers for applicant 150. Multiple offersmodule 309, similar to previous modules, may retrieve stored data fromdatabase 103. Alternatively, multiple offers module 309 may retrievedata from credit bureau computing device 322 via network 316.

The vehicle loan generation module 312 can also call prequalificationmodule 304. Prequalification module 304 includes instructions executedby processor 301 to determine if applicant 150 is qualified to receive avehicle loan based on his collateral. Similar to the credit limitassignment module 306, the prequalification module 304 may retrievecredit data from database 103 or a credit bureau computing device 322via network 316. The retrieved credit data may be used byprequalification module 304 to determine if applicant 150 qualifies fora vehicle loan, based on the value of his collateral. Additionally,prequalification module 304 may also rely on the maximum term, amount,and LTV ratio determined by the credit limit assignment module 306 tomake a prequalification determination.

If the automated underwriting module 302 determines that manualunderwriting is needed, vehicle loan generation module 312 may callskill based routing module 310. Skill based routing module 310 includesinstructions executed by processor 301 to determine which credit analystto use for manual underwriting. Skill based routing module 310 makesthis determination based on loan processing time, analyst expertise, andanalyst availability. Similar to the previous modules, the skill basedrouting module 310 may retrieve data from database 103 and/or creditbureau computing device 322 via a network 316. The skill based routingmodule 310 may also communicate data via a network 316 to anunderwriting organization computing device 324.

Text mining module 311 may be called by any one of the aforementionedmodules to mine the text of retrieved data for entry into the database103. The mined text may also be used by one or more of theaforementioned modules to assist with module execution. For example,vehicle loan generation module 312 may call text mining module 311 tomine a retrieved credit data file about applicant 150 for specificinputs (e.g. FICO score). The mined text (FICO score value) may then bestored at database 103 and used by vehicle loan generation module 312 togenerate vehicle loans for an applicant 150.

The server 102 may be operatively connected to send and receivecommunications, data, requests, and/or responses over the network 316via I/O circuit 315 and network interface 314. The server 102 mayconnect to the network 316 at the network interface 314 via a wired orwireless connection, or other suitable communications technology. Thenetwork 316 may be one or more private or public networks. The network316 may be a proprietary network, a secure public internet, a virtualprivate network or some other type of network, such as dedicated accesslines, plain ordinary telephone lines, satellite links, combinations ofthese, etc. Where the network 316 comprises the Internet, datacommunications may take place over the network 316 via an Internetcommunication protocol, for example.

The server 102 may receive applicant data from one or more applicantcomputing devices 320 via the network 316. The server 102 may alsorequest data from one or more applicant computing devices 320 via thenetwork 316. Alternatively, the applicant computing device 320 mayprovide applicant data to organization computing device 318 over thenetwork 316. Also, the organization computing device 318 may requestdata from the applicant computing device 320 over the network 316. Theapplicant computing device 320 may be a computer, laptop, mobile phone,PDA, tablet, or other computing device that can access the network 316.

The server 102 may receive data from one or more organization computingdevices 318. The server 102 may receive applicant data from one or moreorganization computing devices 318. Also, the organization computingdevice 318 may request and receive data from the server 102 via thenetwork 316. Organization computing device 318 may be a computer,laptop, mobile phone, PDA, tablet, or other computing device that canaccess network 316. In some embodiments, the organization computingdevice 318 is a computing device that belongs to the organization 101 oran agent of the organization 101.

The server 102 may receive credit bureau data from one or more creditbureau computing devices 322 via the network 316. The server 102 mayalso request data from one or more credit bureau computing devices 322via the network 316. The credit bureau data transmitted may be forapplicant 150. The credit bureau computing device 322 may be a computer,laptop, mobile phone, PDA, tablet, or other computing device that canaccess the network 316.

The server 102 may receive underwriting data from one or moreunderwriting organization computing devices 324 via the network 316. Theserver 102 may also request data from one or more underwritingorganization computing devices 324 via the network 316. The underwritingdata transmitted may be for a vehicle loan application for applicant150. The underwriting organization computing device 324 may be acomputer, laptop, mobile phone, PDA, tablet, or other computing devicethat can access the network 316.

In some embodiments, an agent of the organization 101 may receiveapplicant data directly from an applicant via a phone call, face-to-facemeeting, or other method. The agent of the organization 101 may thentransmit the applicant data to the server 102 over the network 316 viatheir organization computing device 318. Organization computing device318 may also permit an agent of the organization 101 to access, modify,update, report, and/or perform some other action on data stored at thedatabase 103 in server 102. Alternatively, an agent of the organization101 may send, access, modify, update, receive, report, and/or performsome other action on the data stored at the server 102 via the userinterfaces 312A or 312B.

The user interfaces 312A and 312B may be used by applicant 150 or anagent of the organization 101 to provide data to the server 102. Theuser interface 312A may be integral to the server 102. Alternatively,the user interface may not be integral to the server 102, such as userinterface 312B. For example, user interface 312B may be a remoteuser-interface at a remote computing device, such as a webpage or clientapplication.

The database 103 may be configured or adapted to store data related tovehicle loan generation system 300. The database 103 may be used tostore various data, including personal data and/or credit data about theapplicant 150, vehicle information, loan performance data, vehicle loandata, credit bureau data, organizational vehicle loan data,organizational vehicle loan research data, underwriting data, and/orother data relevant to the vehicle loan generation system 300. Asmentioned earlier in FIG. 1, the database 103 may be located at server102. Alternatively, the database 103 may be located remotely from server102. Furthermore, parts of the database 103 may be located at the server102 while other parts of the database 103 may be located remotely fromserver 102.

Although only one processor 301 is shown, the server 102 may includemultiple processors 301. Additionally, although the I/O circuit 315 isshown as a single block, the I/O circuit 315 may include a number ofdifferent types of I/O circuits. Similarly, the memory of the server 102may include multiple RAMs 303 and multiple program memories 307.Further, while the instructions and modules are discussed as beingstored in memory 307, the instructions and modules may additionally oralternatively be stored in the RAM 303 or other local memory (notshown).

The RAM(s) 303 and program memories 307 may be implemented assemiconductor memories, magnetically readable memories, chemically orbiologically readable memories, and/or optically readable memories, ormay utilize any suitable memory technology.

Acquisition Scoring Model

The vehicle loan generation system 200 uses the acquisition scoringmodel 210 to determine an acquisition score for applicant 150. Theacquisition score evaluates the credit worthiness of the applicant 150based on various types of credit data. The acquisition score helpsreduce the risk of vehicle loans generated by system 200 for theorganization 101.

FIG. 4 is a block diagram of an acquisition scoring model environment400. The environment 400 includes the acquisition scoring model 210,inputs 406, and outputs 407. The acquisition scoring model 210 receivescredit bureau attributes of an applicant 405 as an input 406.Additionally, the acquisition scoring model 210 sends an acquisitionscore 410 as an output 407. The acquisition scoring model 210 determinesthe output 407 based on the received inputs 406. The subsequent figuresexplain how acquisition scoring model 210 determines outputs 407.Outputs 407 may be used by one or more different models. In oneembodiment, the acquisition score 410 is used by the automatedunderwriting model 220 to determine if an application should be approvedor denied.

In some embodiments, acquisition scoring model 210 may rely on more,less, and/or different inputs than those displayed in FIG. 4. Forexample, acquisition scoring model 210 may rely on applicant applicationdata, loan performance data, internal bureau data, and/or other data todetermine outputs 407. Acquisition scoring model 210 may include more,less, and/or different outputs than those displayed in FIG. 4.Acquisition scoring model 210 may also rely on one or more businessrules to determine outputs 407.

FIG. 5A includes an embodiment of acquisition scoring model inputs table500. The table 500 includes variable description column 505, trade linetype column 510, and variable type column 515. The table 500 alsoincludes eleven rows of variables numbered 520 through 530. In thedisplayed embodiment, the trade line type column 510 includes trade linetypes overall, installment, revolving, finance, auto loan, bankcard, andstudent loan. In some embodiments, more, less, and/or different tradeline types exist. In the displayed embodiment, the variable type column515 displays variable types inquiries, delinquency, vintage, creditamount, utilization, balance, and trades. In other embodiments, more,less, and/or different variable types exist.

The acquisition scoring model inputs table 500 may include more, less,and/or different columns (505, 510, and 515) than those displayed. Also,the acquisition scoring model inputs table 500 may have more, less,and/or different category columns (510, 515) than those displayed.Acquisition scoring model table 500 may also contain more, less, and/ordifferent variables than those described in the displayed embodiment(520 through 530). In FIG. 5A, the variables 520 through 530 are creditbureau attributes of the applicant 405 (see FIG. 4). However, the sourceof the variable inputs could be applicant application data, loanperformance data, internal bureau data, credit bureau data, third-partydata, and/or other data.

Variable 526 describes a key-coded aggregate balance amount for opentrades as it relates to an applicant 150. FIG. 5B includes keycodingtable 550 for variable 526. In some embodiments, variable 526 may havemore, less, and/or different key codes than those displayed in FIG. 5B.Additionally, variable 526 may have more, less, and/or different balanceamounts key coded than those displayed in FIG. 5B. Alternatively,variable 526 may not be key-coded. Also, in FIG. 5A, acquisition scoringmodel inputs table 500 may include more, less, and/or different keycoded variables than those displayed.

FIG. 6 displays an acquisition scoring model contribution table 600. Thetable 600 includes variable description column 505 and contributioncolumn 605. The table 600 also shows variables 520 through 530 alongwith the intercept variable 610. The intercept variable 610 allows anoffset to be included in the acquisition score, if necessary. The valuesin the contribution column 605 represent the numerical weights to assignto a variable when calculating an acquisition score. While the displayedembodiment shows different values assigned to each variable (e.g., “A%”, “B %”, etc.), in some embodiments, the values in the contributioncolumn 605 may be equal for two or more variables displayed in FIG. 6(e.g., variables 520 and 521 both have a value of “A %” in column 605).Also, in some embodiments, the table 600 may include more, less, and/ordifferent variables than those displayed in FIG. 6 (520 through 530). Asa result, there may be more, less, and/or different values incontribution column 605 than the values displayed in FIG. 6.

The table 600 was determined by conducting statistical analysis onvarious data, including credit bureau data, loan performanceinformation, application information, underwriting information, and/orother information related to vehicle loans. In this embodiment, the dataanalyzed was collected over a four year period. However, in otherembodiments, the collected data may span a longer, shorter, and/ordifferent amount of time. The various statistical methods used mayinclude data preparation, target definition, data partitioning, variableclassing, binning, variable reduction, logistic regression, trending,validation, rank ordering, comparison, and/or other statistical methods.The acquisition score determined by model 220 may be a more reliableindicator of the credit worthiness of an applicant than other scores.These scores may include a FICO score, and/or other custom scoresgenerated to estimate an applicant's credit worthiness.

The acquisition score model 220 may rely on more, less, and/or differentmethods to determine an acquisition score. This may include decisiontrees, business rules, and/or statistical methods. Additionally,different equations, different factors, different weights, and/or otherdifferent methods than those displayed in the aforementioned figures maybe used to determine an acquisition score.

Automated Underwriting Model

The vehicle loan generation system 200 also includes automatedunderwriting model 220. The automated underwriting model 220 furtherautomates the underwriting process by automatically approving ordeclining some applications, and referring the remaining applicationsfor manual underwriting. This model reduces the risk an organization 101assumes by automatically declining certain high-risk applications.Additionally, the model 220 reduces the amount of applications requiringmanual underwriting by automatically approving or declining someapplications. The reduction in manual underwriting reduces theturnaround time of some applications. Also, the automatic approval ofapplications by model 220 improves the chances of organization 101 togenerate loan business with those applicants by reducing theirturnaround time. Furthermore, automatic approval of applications mayincrease the number of potential loan customers for organization 101.

FIG. 7 is a block diagram of an automated underwriting environment 700.The automated underwriting environment 700 includes the automatedunderwriting model 220. The automated underwriting model 220 receivesinputs 701, and uses inputs 701 to generate an output 716. The inputs701 may include credit bureau attributes of an applicant 705, applicantapplication data 710, loan performance data 715, and an acquisitionscore 410 for an applicant 150. The output 716 may contain anauto-approve decision 720, an auto-decline decision 725, or a referdecision 730.

The credit bureau attributes of an applicant 705 may or may not bedifferent from the credit bureau attributes of an applicant 405 receivedby acquisition score model 210 in FIG. 4. In some embodiments, theattributes 705 may include a FICO score and/or a bankruptcy score.Additionally, applicant application data 710 and loan performance data715 may or may not be the same as applicant application data and loanperformance data used by the acquisition scoring model 210. The loanperformance data 715 may be stored by the organization 101.Alternatively, the loan performance data 715 may be obtained by theorganization 101 from a third party. The applicant application data 710may be obtained by the organization 101 and/or a third party. Also, insome embodiments, more, less, and/or different inputs 701 may be used bythe automated underwriting model 220 than those displayed in FIG. 7.

The automated underwriting model 220 uses inputs 701 to determine anoutput 716. In the displayed embodiment, the output 716 could be anauto-approve decision 720, an auto-decline decision 725, or a referdecision 730. If an application is automatically approved 720, thecredit limit assignment model is then invoked because the underwritingprocess is complete. Alternatively, if the application is automaticallydeclined 725 (also referred to as automatically denied), no other modelsneed to be called. However, if the application requires manualunderwriting, a refer decision 730 occurs. The skill based routing model260 is then used to improve the manual underwriting process. If aftermanual underwriting the application is approved, the credit limitassignment model 230 is then needed because the underwriting process iscomplete. Alternatively, if after manual underwriting, the applicationis denied, no other models need to be called. While the displayedembodiment includes three possible decisions (auto-approve 720,auto-decline 725, refer 730), other embodiments may include more, less,and/or different decisions than those displayed. Also, other embodimentsmay include more, less, and/or different outputs 716 than those shown inFIG. 7.

In some cases, the automated underwriting model 220 relies on a dualmatrix along with inputs 701 to determine output 716. In other cases,model 220 may use underwriting rules along with inputs 701 to generateoutput 716. Alternatively, model 220 may use decision trees, statisticalmethods, and/or other methods for determining output 716 based on inputs701. Further, the model 220 may use a combination of the aforementionedmethods to determine output 716 based on inputs 701.

Automated Underwriting Model Dual Matrix

FIG. 8 displays an automated underwriting dual matrix 800. The dualmatrix 800 includes a horizontal axis 805 and a vertical axis 810. Dualmatrix 800 also includes an auto-approve region 815, an auto-deny region820, and a refer region 825. Further, dual matrix 800 includes splitregion 830 and split region 835. Split region 830 includes an auto-denyportion and a refer portion. Split region 835, on the other hand, has anauto-approve portion and a refer portion. The dual matrix 800 allows theautomated underwriting model 220 to determine if an application requiresmanual underwriting. Further, dual matrix 800 enables the model 220 toautomatically approve or deny applications that don't require manualunderwriting.

For the dual matrix 800 in FIG. 8, the horizontal axis 805 is for anacquisition score while the vertical axis 810 is for a FICO score.Horizontal axis 805 includes multiple columns of different acquisitionscores. Each column delineates a range of acquisition scores. Forexample, column 806 delineates a range of acquisition scores from 1500to 1549 while column 807 is for acquisition scores from 1550 through1599. Similarly, vertical axis 810 includes multiple rows of differentFICO scores. Each row corresponds to a range of FICO scores. Forexample, row 811 is for a range of FICO scores from 730 through 799,while row 812 covers FICO scores from 800 through 839.

Dual matrix 800 may have different axes than those displayed in FIG. 8.For example, acquisition scores may be plotted on the vertical axis 810while the FICO scores are plotted on the horizontal axis 805.Alternatively, the horizontal axis 805 and vertical axis 810 may havedifferent categories than those displayed in FIG. 8. The horizontal axis805 may have more, less, and/or different columns than those displayed.The vertical axis 810 may have more, less, and/or different rows thanthose displayed.

In the displayed embodiment, the ranges of the columns are unequal. Forexample the range of an acquisition score column for scores between 1700and 1779 is larger than the range of the column for acquisition scoresbetween 1600 and 1639, as displayed in FIG. 8. Unlike the displayedembodiment, the ranges of each column may be equal for most and/or allcolumns. Additionally, unlike the displayed embodiment, the ranges foreach row may also be equal for most and/or all rows.

Dual matrix table 800 includes auto-approve region 815. If an applicanthas an acquisition score and a FICO score that map to a row and columnwithin the auto-approve region 815, then the application isautomatically approved by the automated underwriting model 220. In thiscase, model 220 sends an output 716 (FIG. 7) indicating an auto-approvedecision 720 (FIG. 7). The credit limit assignment model 230 would thenbe called upon to further process the application.

Also, the dual matrix table 800 has an auto-deny region 820. In thiscase, if an applicant has an acquisition score and a FICO score that mapto a row and column within the auto-deny region 820, the application isautomatically denied by model 220. Here, model 220 generates an output716 (FIG. 7) indicating an auto-deny decision 725 (FIG. 7). No othermodels are needed because the underwriting process, along with anysubsequent processing, of this application is complete.

Further, the dual matrix table 800 also contains a refer region 825. Forthis region, if an applicant has an acquisition score and a FICO scorethat map to a row and column within the refer region 825, theapplication is referred for manual underwriting by model 220. In thisscenario, the model 220 sends an output 716 (FIG. 7) dictating a referdecision 730 (FIG. 7). The skill based routing model 260 is called toprocess the referred applications for manual underwriting.

Additionally, the dual matrix table 800 displays a split region 830. Inthe split region 830, part of the region results in an auto-denydecision 725 (FIG. 7) while the remaining part of the region results ina refer decision 730 (FIG. 7). For split region 830, if an applicant hasan acquisition score and FICO score that map to a row and a columnwithin the split region 830, the application may be automatically deniedor referred for manual underwriting by model 220.

In some cases, an underwriting rule is applied to the applicant'sapplication to determine if it should be automatically denied orreferred. If the application is automatically denied, the model 220generates an output 716 (FIG. 7) indicating an auto-deny decision 725(FIG. 7). No other models are needed because the underwriting process,along with any subsequent processing, of this application is complete.Alternatively, if the application is referred for manual underwriting,the model 220 sends an output 716 (FIG. 7) dictating a refer decision730 (FIG. 7). The skill based routing model 260 is called to process thereferred applications for manual underwriting.

Also, the dual matrix 800 shows a split region 835. For split region835, part of the region results in an auto-approve decision 720 (FIG.7), whereas the remaining part of the region results in a refer decision730 (FIG. 7). For split region 835, if an applicant has an acquisitionscore and FICO score that map to a row and a column within the splitregion 835, the applicant's application may be automatically approved orreferred for manual underwriting by the automated underwriting model220.

For region 835, an underwriting rule may be applied to the applicant'sapplication to determine if the application should be automaticallyapproved or referred. If the application is automatically approved, themodel 220 creates an output 716 (FIG. 7) indicating an auto-approvedecision 720 (FIG. 7). The credit limit assignment model 230 would thenbe called upon to further process the application. On the other hand, ifthe application is referred for manual underwriting, the model 220 sendsan output 716 (FIG. 7) dictating a refer decision 730 (FIG. 7). Theskill based routing model 260 is called to process the referredapplications for manual underwriting. FIG. 10 contains an example of howan underwriting rule can be used to resolve a split region 830 or 835.

For the dual matrix 800 and FIG. 8, the regions 815, 820, 825, 830, and835 are predetermined. The regions are predetermined to reduce the risktaken by organization 101 when granting loans to applicants. Thus, theauto-approve region 815 spans acquisition scores and FICO scores thatsuggest a lower likelihood of default. Alternatively, the auto-denyregion 820 covers acquisition scores and FICO scores that may be ahigher risk of default. The refer region 825 and split regions 830 and835 cover acquisition scores and FICO scores that neither suggest a highnor low risk of default. Consequently, further analysis, such as manualunderwriting and/or underwriting rules, may be needed to approve or denythe applicants.

As was discussed in the acquisition scoring model 210, various types ofdata, including credit bureau data, loan performance information,application information, underwriting information, and/or otherinformation related to vehicle loans was analyzed over a time period togenerate models. For model 220, all received applications over an 18month period were analyzed to determine the dual matrix 800 and itsregions 815, 820, 825, 830, and 835. However, applications over adifferent time period, such as 12 months, 24 months, or 36 months, couldhave been collected and analyzed. The statistical methods used on thisdata may include data analysis, historical data, trending, regressionanalysis, rank ordering, and/or other methods of data analysis. In otherembodiments, the regions may be adjusted. For example, if theorganization determines it is taking on too many risky loans, theauto-deny region 820 may be increased. Further, the auto-approve regionmay be decreased. Alternatively, if the collected data that was analyzedis updated, the updated data may warrant a revision to the model 220,the matrix 800, and its regions 815, 820, 825, 830, and 835.

Dual matrix 800 may have more, less, and/or different regions than thosedisplayed in FIG. 8. The regions 815, 820, 825, 830, and 835 may spanmore, less, and/or different acquisition scores than those displayed inFIG. 8. The regions 815, 820, 825, 830, and 835 may overlap more, less,and/or different groups of acquisition scores (i.e. acquisition scorecolumns) than those shown. Additionally, the regions 815, 820, 825, 830,and 835 may cover more, less, and/or different FICO scores than thoseseen in FIG. 8. The regions 815, 820, 825, 830, and 835 may overlapmore, less, and/or different groups of FICO scores (i.e. FICO scorerows) than those shown. Further, the regions 815, 820, 825, 830, and 835may be mapped onto a matrix with criteria that is different from theacquisition score, the FICO score, or both. Also, the regions 815, 820,825, 830, and 835 may be mapped onto a matrix where axes 805 and 810 areswapped, i.e. vertical axis 810 shows acquisition scores whilehorizontal axis 805 displays FICO scores.

Split regions 830 and 835 are shown in FIG. 8 as having two possibleoutputs. For example, split region 830 could have a refer decision 730or an auto-deny decision 725 while split region 835 may have a referdecision 730 or an auto-approve decision 720 (FIG. 7). However, splitregions 830 and 835 could have more, less, and/or different possibleoutputs than those displayed in FIG. 8. For example, a split regioncould lead to any of three decisions, such as an auto-approve decision720, an auto-deny decision 725, or a refer decision 730 (FIG. 7).

In the displayed embodiment of FIG. 8, dual matrix 800 can lead to anoutput 716 of three different decisions, an auto-deny decision 725, anauto-approve decision 720, and a refer decision 730 (FIG. 7). However,in other embodiments, the dual matrix 800 can lead to more, less, and/ordifferent decisions and/or outputs than those shown in FIG. 8. Also,depending on the decision made, the skill based underwriting model 260and/or the credit limit assignment model 230 may be called. However, inother embodiments, more, less, and/or different models are calleddepending on the decision and/or output that is generated.

FIG. 9A displays an automobile negative outcome dual matrix 900. Theautomobile dual matrix 900 includes percentage figure box 905 and blankbox 910. The dual matrix 900 also includes horizontal axis 805, verticalaxis 810, auto-approve region 815, auto-deny region 820, refer region825, and split region 830.

The percentage figure in percentage figure box 905 in the matrix 900represents the percentage of applications where a negative outcomeoccurs for applicants that receive vehicle loans that have theacquisition score and FICO score that correspond to the scores for box905. A negative outcome may be a default by the applicant 150 on avehicle loan. Blank box 910 does not contain a percentage figure becauseno applications from applicants with a FICO score and acquisition scorethat correspond with the scores of blank box 910 exist. While apercentage figure is displayed in FIG. 9, a different data item could beused, such as a number, letter, symbol, and/or other data identifier.For example, a grading system with numbers or letters could be used tocategorize the riskiness of granting certain applicants an auto loan.Box 905 would then display the corresponding grade as opposed to apercentage figure. Blank box 910 would remain blank.

Although FIG. 9A displays a negative outcome rate of dual matrix 900,other types of data could be reflected in a dual matrix 900. Forexample, instead of a negative outcome rate, a positive outcome rate maybe displayed. Additionally, the displayed data may be associated withevents different from a loan default. For example, the displayed datamay be associated with approved loan applications, denied loanapplications, preferred loan applications, manually approved loanapplications, manually denied loan applications, credit limit amounts,collateral amounts, vehicle loans closed, vehicle loans that are notclosed, new customers, existing customers, lost customers, and/or otherbusiness and/or loan performance data. The displayed data may be apercentage, number, fraction, letter, picture, and/or other dataidentifier.

The automotive negative outcome dual matrix 900 displayed in FIG. 9Aprovides insight on how regions 815, 820, 825, and 830 were determined.Specifically, applications that had a lower likelihood of a negativeoutcome generally fell within the auto-approve region 815. Applicationsin region 815 generally had a negative outcome percentage of 3.0% orless, as shown in FIG. 9A. Alternatively, applications with a higherlikelihood of a negative outcome were covered by the auto-deny region820. Applications in region 820 generally had a negative outcomepercentage of 5.1% or higher. Additionally, applications in the referregion 825 and/or the split region 830 generally had negative outcomepercentages in between 3.0% and 5.1%. Thus, the auto-approve region 815generally applies to lower risk applications while the auto-deny region820 generally applies to high-risk applications. Meanwhile, the referregion 825 and split region 830 generally applies to applications thatare neither high nor low risk.

For split region 830, applications with the corresponding acquisitionscore (1700 through 1779) and FICO score (640 through 679) may exhibitdifferent tendencies based on other data. For example, in the displayedembodiment of FIG. 9A and FIG. 10, an underwriting rule may be appliedto applications falling within split region 830 to determine if theyshould be automatically denied or referred. By using an underwritingrule to analyze additional data about the applications, higher riskapplications are identified and automatically denied while lower riskapplications are referred. Specifically, the applications that wereautomatically denied exhibited a 5.0% negative outcome percentage, whileapplications that were referred only exhibited a 4.1% negative outcomepercentage. Thus, examining additional data about these applicationsallowed higher risk applications to be removed from consideration whilethe remaining applications were referred for manual underwriting.

FIG. 9B shows a recreational vehicle and boats negative outcome dualmatrix 950. Similar to the automobile negative outcome dual matrix 900,dual matrix 950 includes a percentage figure box 955, a blank box 960, ahorizontal axis 805 for acquisition scores, a vertical axis 810 for FICOscores, an auto-approve region 815, an auto-deny region 820, a referregion 825, and a split region 830. Similar to matrix 900, theauto-approve region generally spans applications exhibiting a negativeoutcome rate of 3% or lower, the auto-deny region 820 coversapplications with a negative outcome rate of 5.0% or higher, and therefer region 825 and split region 830 cover the remaining applications.Further, the split region 830 is further divided to identify high-riskapplications that should be denied and the remaining applications thatshould be referred.

Although FIG. 9B shows a negative outcome dual matrix 950 forrecreational vehicles and boats, a dual matrix can be generated for avariety of different types of products. The products could includevehicles, recreational vehicles, automobiles, boats, motorcycles,trucks, scooters, and/or other vehicle loan products. Additionally, thedual matrix could be for different categories of applications, such asnew vehicles, used vehicles, vehicles of a certain brand, vehicles of acertain model, vehicles from a certain manufacturer, vehicles fromcertain regions, vehicles from certain time periods, vehicles of acertain price, vehicles of a certain size, vehicles of a certain type(luxury vehicles, sport-utility vehicles, sports cars, sedans, and/orconvertibles), and/or other categories. The pool of applicants availablefor the dual matrix may also be modified. For example, instead ofshowing all applicants, the matrix 950 may show data for only newapplicants, repeat applicants, current customer applicants, targetcustomers, potentially new customers, and/or other categories ofapplicants and customers.

FIG. 10 is a diagram of a dual matrix split region decision tree 1000.The decision tree 1000 includes box identifier 1005, region identifier1010, underwriting rule 1015, underwriting rule criteria 1020,underwriting rule criteria 1030, split region tree decision 1025, andsplit region tree decision 1035. In some embodiments, the dual matrixsplit region decision tree 1000 includes more, less, and/or differentitems than those displayed in FIG. 10. In some embodiments, the dualmatrix split region decision tree 1000 includes the same parts used in adifferent order than the order displayed in FIG. 10.

Decision tree 1000 includes box identifier 1005. The box identifierindicates the acquisition score band and FICO score band that areapplicable for the corresponding split region. In the displayedembodiment, the acquisition score band is 1640 through 1699, while theFICO score band is 680 through 729. In other embodiments, theacquisition score band is more, less, and/or different than the banddisplayed. Additionally, the FICO score band may be more, less, and/ordifferent than the band displayed. Further, the box identifier 1005 mayinclude criteria that is different from the acquisition score and/or theFICO score. This would occur if the dual matrix contains axes that relyon criteria different from the acquisition score and/or the FICO score.

Decision tree 1000 also contains a region identifier 1010. In thedisplayed embodiment, the region identifier 1010 shows the region to bethe refer region. However, the region identified could be a differentregion than that displayed in FIG. 10.

Decision tree 1000 relies on underwriting rule 1015 to further identifyapplications that should be automatically approved and/or automaticallydenied. In FIG. 10, the underwriting rule used for the applicant is the“number of non-auto loan and lease inquiries within 12 months” by theapplicant. However, different underwriting rules may be used to identifyapplications that should be automatically approved and/or automaticallydenied. Further, the decision tree 1000 may use multiple underwritingrules to identify applications that can automatically be approved ordenied.

Decision tree 1000 also uses underwriting rule criteria 1020 and 1030 toresolve split regions. In the displayed embodiment of FIG. 10,underwriting rule criteria 1020 specifies that the number of non-autoloan and lease inquiries within 12 months for an applicant is less thansix, whereas criteria 1030 specifies that number to be greater than orequal to six. If the applicant has fewer than six non-auto loan andlease inquiries over the last 12 months, the applicant satisfiescriteria 1020, which then causes split region tree decision 1025 tooccur. In this case, the applicant's application is referred for manualunderwriting 1025. Alternatively, if the applicant has six or morenon-auto loan lease inquiries over the past 12 months, then criteria1030 are satisfied. This causes the split tree region decision 1035 tooccur. As a result, applicant's application is automatically denied1035.

Although the displayed embodiment contains two underwriting rulecriteria 1020 and 1030, more, less, and/or different criteria can beused with the underwriting rule to resolve a split region. Further,satisfaction of the underwriting rule criteria 1020 and 1030 leadsdirectly to decisions 1025 and 1035. However, in some embodiments,satisfaction of one or more criteria may cause the evaluation of one ormore additional underwriting rules before leading to a decision 1025 or1035. Also, the decisions 1025 and 1035 that are caused by criteria 1020and 1030 being satisfied may be different than those displayed in FIG.10. For example, decision 1025 could be an auto-approve decision.Further, while the displayed embodiment shows two criteria 1020 and 1030along with two decisions 1025 and 1035, more criteria and decisions maybe possible than just those displayed. For example, a split region mayhave three sets of criteria to make three different decisions, such asan auto-approve decision, a refer decision, and an auto declinedecision. Additionally, more than three sets of criteria and threedecisions are possible.

FIG. 11 displays the automated underwriting model business rules table1100. Table 1100 shows various business rules that can be applied tohelp determine whether an applicant should be automatically approved,automatically denied, or referred for manual underwriting. The tableincludes a business rule number 1105, a business rule type 1110, and thebusiness rule description 1115 for each business rule in the table 1100.In the displayed embodiment, the business rule types 1110 includeprocess, policy, and business. However, model 220 may include more,less, and/or different business rule types than those shown. Also, model220 may have more, less, and/or different business rules than thosedisplayed in table 1100.

Business rules may be used by the automated underwriting model 220 toresolve split regions in a dual matrix, as shown in FIG. 10.Additionally, model 220 may rely on business rules to filterapplications before using the dual matrix. For example, if one or moreof a set of business rules are not satisfied, an application may beautomatically denied, automatically approved, or referred for manualunderwriting before ever using a dual matrix. Alternatively, if one ormore of a set of business rules are satisfied, an application may beautomatically approved, automatically denied, or referred for manualunderwriting before ever using a dual matrix.

Although not displayed, in some embodiments, the automated underwritingmodel 220 also uses a bankruptcy score filter to further determinewhether an applicant should be automatically denied for vehicle loanapplication underwriting. In one embodiment, the bankruptcy score uses ascale from 1 to 800, with a higher score indicating higher creditworthiness of an applicant. In some embodiments, the model 220automatically denies vehicle loan underwriting for applicants with abankruptcy score below 600. However, bankruptcy score filters usingdifferent bankruptcy score scales, cutoffs, and/or underwritingdecisions are also possible.

By including a bankruptcy score filter into model 220, an organizationcan further remove risky applicants from a pool of potential vehicleloan applicants. For example, if a vehicle loan applicant had anacquisition score of 1723, a FICO score of 823, and a bankruptcy scoreof 500, a model 220 incorporating the aforementioned bankruptcy scorefilter would automatically deny the vehicle applicant (bankruptcy scoreof 500 is less than 600), whereas a model without the bankruptcy scorefilter would automatically approve the applicant (based on the dualmatrix of FIG. 8). Thus, a model 220 with a bankruptcy score filterreduces the risk incurred by an organization that generates vehicleloans for potential applicants.

Automated Underwriting Model Examples

In one example, John Doe (applicant 150) submits an applicationrequesting vehicle loan financing. An acquisition score is assigned toJohn Doe based on his application by acquisition scoring model 210.Automated underwriting model 220 is then called to determine if John Doeshould be automatically approved, automatically denied, or referred formanual underwriting. In this example, John Doe has been assigned anacquisition score of 1723 and has a FICO score of 823. Using dual matrix900, the appropriate column for John Doe's acquisition score of 1723 isthe column with the range of 1700 through 1779 on the acquisition scorehorizontal axis 805. The appropriate row has a range of 800 through 839on the FICO score vertical axis 810. The corresponding box indicates anegative outcome rate of 1%. Also, the corresponding box falls withinthe auto-approve region 815. As a result, automated underwriting model220 generates an output 716 to automatically approve 720 John Doe'sapplication. As a result, credit limit assignment model 230 is thencalled upon to further process John Doe's application.

In another example, John Doe has an acquisition score of 1723 and a FICOscore of 623. Based on dual matrix 900, the appropriate column for JohnDoe's acquisition score of 1723 remains the column with the range of1700 to 1779 on the acquisition score horizontal axis 805. Theappropriate row, however, now has a range of FICO scores less than 640on the vertical axis 810. The corresponding box indicates a negativeoutcome rate of 5.2%. Further, the corresponding box falls within theauto-deny region 820. Consequently, automated underwriting model 220generates an output 716 to automatically deny 725 John Doe'sapplication. No other models are called upon because the underwritingprocess is complete and no other processing is needed for John Doe'sapplication.

In yet another example, John Doe has an acquisition score of 1673 and aFICO score of 693. According to dual matrix 900, the appropriate columnfor John Doe's acquisition score of 1673 is the column with the range of1640 through 1699 on the acquisition score horizontal axis 805. Theappropriate row has a range of 680 through 729 on the FICO scorevertical axis 810. The corresponding box is a split region 830. Toresolve the split region 830, a dual matrix split region decision tree1000 can be used. The decision tree 1000 uses box identifier 1005 andregion identifier 1010 to locate and determine that John Doe'sapplication is in the split region 830. The decision tree 1000 thenrelies on underwriting rule 1015 to resolve the split region 830. In onecase, John Doe has had five non-auto loan and lease inquiries within 12months. As a result, criteria 1020 is satisfied which leads to decision1025, which is refer. Consequently, model 220 produces output 716 with arefer decision 730. This in turn causes skill based routing model 260 tobe called upon to further process John Doe's application for manualunderwriting.

In another case, John Doe has had 10 non-auto loan and lease inquirieswithin 12 months. This in turn satisfies criteria 1030, which thencauses decision 1035, which is an auto decline decision. As a result,model 220 generates output 716 with an auto-deny decision 725. No othermodels are called upon because the underwriting process is complete andno other processing is needed for John Doe's application.

In one more example, John Doe has an acquisition score of 1785 and aFICO score of 600. Based on dual matrix 900, the appropriate column forJohn Doe's acquisition score of 1785 is the column with the range of1780 through 1869 on the acquisition score horizontal axis 805. Theappropriate row has the range of FICO scores less than 640 on the FICOscore vertical axis 810. The corresponding box displays a negativeoutcome rate of 3.6%. Additionally, the corresponding box falls withinthe refer region 825. Because of this, the automated underwriting model220 creates an output 716 with the refer decision 730. This causes skillbased routing model 260 to be called to further process John Doe'sapplication for manual underwriting.

In another example, John Doe is 17 years old and has a FICO score of 800and has been assigned an acquisition score of 1800. In the automatedunderwriting model business rules table 1100, rule number seven (1105)is a policy rule (1110) with a description (1115) that states primaryand secondary (if any) age is greater than or equal to 18. In otherwords, this business rule fails if none of the applicants are 18 yearsof age or older. Because John Doe is 17 years old, this business rule isnot satisfied. As a result, John Doe's application is automaticallydenied and dual matrix 900 is not needed. The automated underwritingmodel 220 generates an output 716 with an auto-deny decision 725. Noother models are called upon because the underwriting process iscomplete and no other processing is needed for John Doe's application.

Credit Limit Assignment Model

The vehicle loan generation system 200 also includes the credit limitassignment model 230. The purpose of the credit limit assignment model230 is to determine limits on potential loans for an applicant 150.These limits include the maximum term (length) of the loan, the maximumloan to value (LTV) ratio, and the maximum monthly payment capacity ofthe applicant 150. The goals of these limits include meeting the needsof the applicant 150 and reducing the risk taken by organization 101.These objectives are often in tension because the applicant 150 may wanta larger amount lent for a longer period of time than what theorganization 101 deems worthy, based on the value of the collateral andapplicant's credit worthiness. Credit limit assignment model 230attempts to strike the appropriate balance between the applicant's andorganization's needs.

FIG. 12 is a block diagram of a credit limit assignment modelenvironment 1200. In environment 1200, credit limit assignment model 230receives inputs 1205 and generates outputs 1260. Credit limit assignmentmodel 230 includes the models term determination model 1240, loan tovalue (LTV) cut off determination model 1245, and payment capacity model1250. These models process inputs 1205 to generate outputs 1260. Theinputs 1205 received by credit limit assignment model 230 includesvehicle information 1210, credit bureau data 1220, loan information1230, and acquisition score 410. The outputs 1260 generated by creditlimit assignment model 230 include the maximum term 1270, the maximumLTV ratio 1275, and maximum payment capacity 1280.

The vehicle information 1210 received by model 230 may include thevehicle mileage, whether the vehicle is new or used, purchase price,condition, manufacture, brand, model, performance specifications (enginepower, fuel efficiency, security features, etc.), and/or otherinformation about the vehicle. The vehicle information 1210 may be aboutthe collateral and/or the financed vehicle. The credit bureau data 1220may be attributes regarding the applicant 150. The bureau data 1220 maybe the same as, different from, more than or less than the credit bureauattributes 705 (FIG. 7) and/or attributes 405 (FIG. 4) for applicant150. The credit bureau data 1220 may include the FICO score for theapplicant 150. Loan information 1230 may be information regarding theapplicant 150. Loan information 1230 may be the same as, more than, lessthan, or different from the loan performance data 715 (FIG. 7) and/orloan performance data used by the acquisition scoring model 210. Whilethe displayed embodiment shows acquisition score 410 as an input, inother embodiments, other custom scores indicating credit worthiness ofthe applicant 150 may be used. The inputs 1205 may have more, less,and/or different inputs than those displayed in FIG. 12.

The term determination model 1240 processes inputs 1205 to determine themaximum term 1270. The maximum term 1270 states the maximum lengthallowed for offered loans to an applicant 150. The maximum term may beexpressed in months, years, or other applicable units of time. The LTVcut off determination model 1245 generates a maximum LTV ratio 1275. Theratio 1275 is a limit on the ratio between the amount of the loandivided by the value of the financed vehicle and/or collateral.Generated loans for a vehicle may not have an LTV ratio that exceeds theratio 1275. Payment capacity model 1250 outputs the maximum paymentcapacity 1280. The maximum payment capacity 1280 estimates the maximummonthly payment that can be required of applicant 150. Alternatively,maximum payment capacity 1280 could be calculated for a yearly payment.The maximum payment capacity 1280 caps the monthly payment from anapplicant 150 for a generated vehicle loan.

Alternatively, the model 1250 may output a post loan debt to income(DTI) cut off for applicant 150. DTI is a percentage equal to theapplicant's debt obligations (such as a monthly payment for debt)divided by the applicant's income (such as monthly income). The postloan DTI cut off is the maximum DTI percentage the applicant may haveafter receiving the loan. Outputs 1260 may have more, less, and/ordifferent outputs than those displayed in FIG. 12. Also, credit limitassignment model 230 may include more, less, and/or different modelsthan those displayed.

Term Determination Model

FIG. 13A is a block diagram of the term determination model environment1300. The environment includes term determination model 1240. The model1240 generates the maximum term output 1270. When generating thisoutput, the model 1240 considers several factors. These factors includerisk 1310, product type 1320, vehicle condition 1330, and/or policyguidelines 1340. Although the FIG. 13A only shows the above factorsmentioned, the model 1240 may consider more, less, and/or differentfactors than those displayed. Also, the model 1240 may output differentand/or more outputs than those displayed.

FIG. 13B shows the vehicle maximum term chart 1350. The chart 1350 canbe used to determine the maximum term associated with an applicant witha particular FICO score getting a loan for a type of vehicle. The chart1350 includes FICO scale 1355, used automobile row 1361, new automobilerow 1362, recreational vehicles and boats row 1363, and other productsrow 1364. Each row includes multiple risk segments, including low risksegments 1356, medium risk segment 1357, and high risk segment 1358.Some of the rows, such as used automobile row 1361 and recreationalvehicles and boats row 1363, also include a very high risk segment 1359.All segments (1356, 1357, 1358, and 1359) include a maximum term value1360.

FICO scale 1355 displays various FICO scores, including scores below 695all the way through scores above 815. Additionally, FICO scale 1355shows specific FICO scores, such as FICO score 695, 760, 815, etc., toindicate boundaries for various risk segments (1356, 1357, 1358, 1359).For example, FICO score 700 is shown because for recreational vehicleand boat products 1363, the very high risk segment 1359 is for FICOscores below 700. Additionally, the high risk segment 1358 for theproduct 1363 is for FICO scores between 700 and 760. Thus, FICO score700 is displayed on FICO scale 1355. In some embodiments, more, less,and/or different scores are displayed on the scale 1355. Also, acriteria different from FICO scores may be used and/or displayed onscale 1355.

Rows 1361, 1362, 1363, and 1364 permit vehicle type to be factored inwhen determining risk segments for various FICO scores along with theassociated maximum terms. In some embodiments, more, less, and/ordifferent vehicle types than those shown in FIG. 13B are used. In otherembodiments, a different criteria for vehicle type is used for the rows1361 through 1364.

In FIG. 13B, risk segments 1356, 1357, 1358, and 1359 span a range ofFICO scores along scale 1355 for a specific product shown in rows 1361through 1364. For example, the medium risk segment 1357 associated withused automobiles 1361 covers applicants with FICO scores between 735 and790. In some embodiments, the chart 1350 includes more, less, and/ordifferent risk segments than those displayed in FIG. 13B. For example,the chart could include a very low risk segment (not displayed in FIG.13B). In some embodiments, the risk segments 1356, 1357, 1358, and 1359may span smaller, larger, and/or different ranges of FICO scores thanthose displayed. Alternatively, the risk segments 1356, 1357, 1358, and1359 may be associated with criteria different from FICO scores and/orvehicle product types.

Each risk segment includes a maximum term value 1360 associated withthat risk segment. Although the terms 1360 are displayed in months,other applicable units of time (for example, years) may be used toindicate the maximum term allowed for a vehicle loan for the associatedrisk segment. Risk segmentation is further explained in FIGS. 14Athrough 14C while term determination is further explained in FIG. 15.

Risk Segmentation

FIG. 14A is an example of an automobile risk segmentation environment1400 for the term determination model 1240. FIG. 14B is an example of arecreational vehicle and boat risk segmentation environment 1405 formodel 1240. FIG. 14C is an example of an “other product” risksegmentation environment 1406 for model 1240. FIG. 19A is an example ofa used car risk segmentation environment 1900 for the loan to value(LTV) cut off model 1245. FIG. 19B is an example of a new car risksegmentation environment 1901 for the model 1245. FIG. 19C is an exampleof a recreational vehicle and boat risk segmentation environment 1902for the model 1245. FIG. 19D is an example of an “other products” risksegmentation environment 1903 for the model 1245.

Environments 1400, 1405, 1406, 1900, 1901, 1902, and 1903 include adecision tree 1401 and inputs 1205. The received inputs 1205 by decisiontree 1401 include vehicle attributes 1210, loan performance 1215, andbureau attributes 1220. The decision tree 1401 includes a product typedecision 1410, a vehicle condition decision 1415 (only for FIGS. 14A,19A, and 19B), and a FICO score decision 1420. Decision tree 1410 alsoshows low risk segments 1356, medium risk segments 1357, high risksegments 1358, and a very high risk segment 1359. For FIGS. 14A, 19A,and 19B, the product type decision 1410 is a car. For FIGS. 14B and 19C,the product type decision 1410 is a recreational vehicle or a boat. ForFIGS. 14C and 19D, the product type decision 1410 is an “other product”.For FIGS. 19A-19D, decision tree 1401 also includes collateral valuedecision 1921. Additionally, for FIGS. 19A and 19B, decision tree 1401includes purchase type decision 1922.

In some embodiments, more, less, and/or different risk segments areshown than those displayed. For example, a decision tree 1410 mayinclude a very low risk segment. Also, more, less, and/or differentcriteria are used than the criteria displayed (1410, 1415, 1420, 1921,1922). For example, a decision tree 1410 may not factor in vehiclecondition decision 1415 (see FIGS. 14B, 14C, 19C, and 19D).Alternatively, a decision tree 1410 may also consider the purchase type(dealer purchase vs. refinancing, see FIGS. 19A and 19B). Additionally,a decision tree 1410 may consider collateral value (see FIGS. 19A-19D).Further, each decision may consider more, less, and/or differentdecisions than those displayed. For example, FICO score decision 1420may include more, less, and/or different ranges of FICO scores thanthose shown. Additionally, vehicle condition decision 1415 may includemore, less, and/or different vehicle conditions than those shown. Forexample, the vehicle conditions could be new, 1-5 years old, and over 5years old. Collateral value decision 1921 may include more, less, and/ordifferent collateral values than those shown. Also, purchase typedecision 1922 may include more, less, and/or different purchase typesthan those displayed. The decision tree 1401 may receive more, less,and/or different inputs 1205 than those displayed.

As has been mentioned earlier, the vehicle loan generation system 200attempts to achieve two goals. First the system 200 seeks to reduce therisk assumed by organization 101 when loans are generated. Second, thesystem 200 attempts to increase the amount of vehicle loan businessgenerated for organization 101. These two goals are often in tension, aspotential customers (applicants) often want loans that are riskier thanwhat the organization 101 wants to provide. Thus, the organization 101typically must balance the increased vehicle loan business generated andthe additional risk assumed by the organization 101. Risk segmentationenvironments 1400, 1405, and 1406 for term determination model 1240 aimto achieve this balance by setting different maximum terms for differentvehicle loans and different applicants based on the risk the vehicleloan and the applicant pose for the organization 101. Meanwhile,environments 1900, 1901, 1902, and 1903 for the LTV cut off model 1245seek to accomplish this balance by setting various LTV cutoffs fordifferent vehicle loans and different applicants based on the riskassociated with the vehicle loan and the applicant for the organization101.

In order to determine how to create risk segments to achieve thesegoals, previously funded vehicle loan applications over a time periodwere analyzed. In the displayed embodiment of FIGS. 14A-15C and 19A-20C,the time period is 18 months. The applications were analyzed todetermine various statistics about the performance of the loans. Thestatistics include total number of applications funded (# Apps),percentage of applications (% Pop), negative outcome rate (#Bad Rate),charge off amount (CO), and charge off rate ($CO Rate), all of which aredisplayed in FIGS. 14A-15C and 19A-20C. Additionally, other loanperformance statistics can be determined, including collateral values,terms, amounts leant, etc.

For the statistics displayed in FIGS. 14A-14C and 19A-9D, the percentageof applications (% Pop) variable is a percentage equal to the number ofapplications for that specific category divided by the total number ofapplications funded (# Apps). The negative outcome rate (#Bad Rate) fora specific category is a percentage equal to the number of applicationswith a negative outcome divided by the applications for that specificcategory (total number of applications (# Apps) multiplied by percentageof applications (% Pop)). In this case, a negative outcome is a loandefault by applicant 150 for FIGS. 14A-15C and 19A-20C. Charge offamount (CO) is the amount of money outstanding on the loan that goesunpaid when the loan defaults. The charge-off rate ($CO Rate or $charge-off rate) for a specific category is a percentage equal to thecharge off amount for that specific category divided by the total amountfunded for the applications for that specific category.

Risk segments were determined based on the applicant's FICO score forFIGS. 14A-15C and 19A-20C. The applications were divided into risksegments such that each risk segment had a significant percentage ofapplications (% Pop) and the negative outcome rate and charge-off rateincreased when comparing a lower risk segment to a higher risk segment.Although FICO scores are used in the displayed embodiments, othercriteria could have been used for determining risk segments.

For example, in FIG. 14A for a used vehicle, the negative outcome rateand charge-off rate increases for each increase in risk. In other words,the negative outcome and charge-off rates for low risk segment 1356(0.60% and 0.32%, respectively) are less than those rates for mediumrisk segment 1357 (1.65% and 0.84%, respectively), which are less thanthose rates for high risk segment 1358 (2.83% and 1.38%, respectively),which are also less than those rates for very high risk segment 1359(3.69% and 1.85%, respectively). Similarly, for FIGS. 14B, 14C, and19A-19D, the negative outcome and charge off rates also increase foreach risk segment increase. Further, each risk segment has a significantpercentage of applications for FIGS. 14A-14C and 19A-19D.

Once risk segments are determined, the maximum terms and appropriate LTVcut offs for each risk segment can then be decided. The subsequent FIGS.15A-15C describe how these maximum terms are calculated for new cars.FIGS. 19A-19D explain how LTV cut offs are determined for used carloans. Also, the above risk segmentation methods disclosed may beapplicable to subsequent risk segmentation displayed and subsequentfigures for other parts of the vehicle loan generation system 200.

Term Determination

FIG. 15A displays a new car low risk segment graph 1500. FIG. 15B showsa new car medium risk segment graph 1501, while FIG. 15C provides a newcar high risk segment graph 1502. The graphs 1500, 1501, and 1502 fromFIGS. 15A, 15B, and 15C, respectively, include a term horizontal axis1505, a negative outcome and charge-off rates vertical axis 1510, and acapture rate vertical axis 1515. Also, graphs 1500, 1501, and 1502 plota cumulative percentage of loans captured line 1520 (% Loans Captured),a cumulative percentage negative outcome captured line 1521 (% BadCaptured), a cumulative negative outcome rate line 1522 (#Bad Rate), anda cumulative charge-off rate line 1523 ($CO Rate). Graph 1500 includes amaximum term line 1530. Also, graph 1501 has a maximum term line 1531,while graph 1502 contains a maximum term line 1532.

In some embodiments, the graphs 1500, 1501, and 1502 include more, less,and/or different axes and lines than those displayed. In someembodiments, the axes displayed contain groupings and/or divisions more,less, and/or different than those displayed. For example, axis 1505 maybe grouped for every 6, 18 or 24 months, as opposed to the displayed 12months. Additionally, axis 1510 may have divisions of every 0.25%,instead of the displayed 0.5%, as seen in FIG. 15C. Further, axis 1515may show divisions of 10%, as opposed to the displayed divisions of 20%.Also, the axes may be displayed in different units than those displayed.For example, axis 1505 may be displayed in years, as opposed to months.

When determining appropriate maximum terms for each risk segment, theorganization 101 may set terms to generate additional vehicle loanbusiness while reducing risk. As a result, the organization 101 mayadopt a general strategy to set larger maximum terms for lower risksegments and smaller maximum terms for higher risk segments. By settinga large maximum term for a low risk segment, the organization 101improves its chances to secure vehicle loan business from the lowestrisk applicants by providing them flexibility with their loan term. Asthe risk of the applicant increases, the organization 101 may reduce themaximum term. By doing so, the loan term options available to riskierapplicants reduce. As a result, the riskier applicant is less likely totake a vehicle loan with the organization 101. This helps limit the riskabsorbed by the organization 101. FIGS. 15A-15C provide examples of howthis strategy is pursued by an organization 101 via the termdetermination model 1240. In other embodiments, other strategies may beadopted by an organization to improve vehicle loan generation whilereducing risk.

FIG. 15A displays the new car low risk segment graph 1500. The maximumterm line 1530 specifies a maximum term of 84 months. According to line1520, nearly 100% of the vehicle loan applicants in the low risk segment1356 for new cars could receive a loan due to the high maximum term of84 months. As a result, the organization 101 improves their chances toattract applicants within the low risk segment 1356. Additionally,because the negative outcome rate 1522 and charge-off rate 1523 at themaximum term of 84 months is relatively low (0.46%, 0.17%,respectively), the organization 101 effectively limits the amount ofrisk it absorbs. However, the organization 101 could further limit therisk it absorbs by reducing the maximum term. For example, the maximumterm could drop from 84 months to 72 months, which would slightly reducethe negative outcome rate to 0.45% (see line 1522) and the charge-offrate to 0.14% (see line 1523). However, this would potentially reducevehicle loan business, as seen by the loans captured dropping to 93.9%(see the cumulative percentage of loans captured line 1520).

FIG. 15B shows the new car medium risk segment graph 1501. The maximumterm line 1531 specifies a maximum term of 72 months. According to line1520, nearly 91% of the vehicle loan applicants in the medium risksegment 1357 for new cars could receive a loan due to the high maximumterm of 72 months. Furthermore, over 95% of the applicants in the mediumrisk segment have a loan term within 12 months of the maximum term 72months, as specified by the maximum term line 1531. As a result, theorganization 101 has a very good chance of attracting applicants withinthe medium risk segment. Additionally, the organization 101 limits itsrisk exposure by reducing the term from 84 months to 72 months. This canbe seen by the lower negative outcome rate at 72 months (1.31%) versus84 months (roughly 1.32%) on line 1522. This can also be seen by thelower charge off rate at 72 months (0.49%) versus 84 months (roughly0.50%) on line 1523. While the displayed embodiment shows a maximum termline 1531 of 72 months for graph 1501, the organization could specify alower maximum term to further reduce the risk absorbed. For example, amaximum term of 60 months would significantly reduce both the negativeoutcome rate (roughly 1.20%, see line 1522) and the charge-off rate(roughly 0.24%, see line 1523). Alternatively, maximum term line 1531could be increased to recruit additional applicants, as seen bycumulative percentage of loans captured line 1520. Specifically,increasing the term line 1531 from 72 months to 84 months would increasethe percentage of loans captured to nearly 100%.

FIG. 15C includes the new car high risk segment graph 1502. For thisgraph, the maximum term line 1532 designates a maximum term of 60months. Consequently, the organization 101 greatly limits its riskexposure. Specifically, the charge-off rate at 60 months is only 0.49%as opposed to roughly 0.80% at 72 months (see line 1523). Also, thenegative outcome rate at 60 months is 2.86%, as opposed to roughly 2.9%at 72 months (see line 1522). Additionally, line 1520 shows that only43% of loans are captured within this segment and a maximum term of 60months, as opposed to 91% for a maximum term of 72 months. This furtherreduces the amount of high risk segment applicants that receive loansfrom the organization. However, as mentioned with FIGS. 15A and 15B, ifthe organization 101 chooses a more aggressive strategy to generateadditional vehicle loan business, the organization could increase themaximum term from 60 months to 72 months to more than double thepotential vehicle loan applicants within this segment (43% to 91%),albeit at a greater risk to the organization 101.

While FIGS. 15A-15C demonstrate for new car vehicle loans how a strategyto generate additional loan business while reducing risk can affect themaximum terms for different risk segments, similar results and strategicdecisions can also be seen when analyzing collected data for otherproducts. This includes used cars, recreational vehicles, boats, and/orother products. This may also include dealer purchase vehicles,refinanced vehicles, and/or other vehicles.

Also, results similar to those displayed in FIGS. 15A-15C may be seenfor risk segments different than those displayed. For example, a veryhigh risk segment 1359, and/or a very low risk segment, may also haveassociated vehicle loan data that can be charted into a graph. The graphdata may also provide insight as to the effects of different maximumterms on the amount of business generated (via loans captured) and/orthe amount of risk absorbed by the organization (via negative outcomerates and charge-off rates).

Furthermore, as discussed earlier, the risk segmentation and dataanalysis displayed in FIGS. 14A-15C are based on data collectedregarding vehicle loans that were funded over an 18 month period. Insome embodiments, new data may be collected and analyzed to update therisk segmentation and term determination from model 1240. As a result,the term determination model 1240 can be adjusted for various reasonsincluding updated data requiring new risk segmentation and termdetermination, altered tolerance for risk by an organization 101,modified requirements for vehicle loan generation by an organization101, and/or other reasons.

Policy Guidelines for Term Determination

FIG. 16 displays policy guidelines 1340 for term determination model1240. The term determination model 1240 outputs a maximum term 1270 thatcomplies with policy guidelines 1340. In some cases, the termdetermination model 1240 adjusts the maximum term value 1360 from chart1350 for the outputted maximum term 1270 to comply with policyguidelines 1340. The policy guidelines 1340 displayed include a productcategory 1605 and a policy guidelines description 1610. Product category1605 includes automobiles, recreational vehicles and boats, otherproducts, and all products. In some embodiments, more, less, and/ordifferent product categories are included in the policy guidelines 1340.For example, the product category 1605 could also include motorcycles.In other embodiments, the policy guidelines 1340 include more, less,and/or different categories than those displayed. For example, thepolicy guidelines 1340 may also include a category for the age of thefinanced vehicle. Also, in some embodiments, more, less, and/ordifferent policy guidelines 1340 for term determination model 1240exist.

Term Determination Examples

In one example, John Doe (applicant 150) submits an application toobtain a vehicle loan for a used automobile. Upon receiving theapplication, organization 101 requests credit data from credit bureau160 regarding John Doe. Organization 101 receives the credit data aboutJohn Doe from credit bureau 160 and analyzes it to determine John Doe'sFICO score. In this example, John Doe has a FICO score of 775. The FICOscore for John Doe is then stored in database 103 located at server 102.

After determining during underwriting that John Doe's application isapproved, the vehicle loan generation system 200 then calls the creditlimit assignment model 230. The credit limit assignment model relies onterm determination model 1240 to calculate a maximum term for John Doe.Term determination model 1240 assigns a maximum term in accordance withchart 1350. John Doe's loan application for a used car corresponds withrow 1361. His FICO score of 775 places him in the medium risk segment1357 of row 1361. The maximum term value 1360 listed for this risksegment 1357 is 72 months. Chart 1350 incorporates the factors risk1310, product type, 1320, and vehicle condition 1330 when determiningthe maximum term. In this example, the maximum term of 72 months doesnot violate any of the policy guidelines 1340. Thus, term determinationmodel 1240 outputs maximum term 1270 as 72 months.

In another example, John Doe's vehicle loan application is for a usedcar with the model year of 2004. John Doe's FICO score remained 775.Similar to the aforementioned example, the credit limit assignment model230 relies on term determination model 1240 to calculate a maximum term.Once again, based on chart 1350, John Doe's FICO score of 775 places hisvehicle loan application for a used car from 2004 in medium risk segment1357 of row 1361, which has a maximum term value 1360 of 72 months.However, maximum term value 1360 of 72 months for a used car from 2004violates policy guidelines 1340. Specifically, policy guidelines 1340dictate that used cars from 2006-07 or earlier are limited to a maximumterm of 60 months. As a result, term determination model 1240 outputs amaximum term 1270 of only 60 months (as opposed to 72 months).Consequently, the maximum term 1270 is in accordance with both chart1350 and policy guidelines 1340.

In yet another example, John Doe's FICO score is 680 and his vehicleloan application is for a boat. After approving John Doe's applicationduring the underwriting process, the credit limit assignment model 230uses the term determination model 1240 to calculate a maximum term 1270in accordance with chart 1350. John Doe's vehicle loan application for aboat places him in row 1363 of the chart 1350. His FICO score of 680intersects with the very high risk segment 1359 of row 1363. Theassociated maximum term value 1360 for this very high risk segment 1359is 60 months. In this example, the maximum term of 60 months does notviolate any of the policy guidelines 1340. Thus, term determinationmodel 1240 outputs maximum term 1270 as 60 months.

Loan to Value (LTV) Cut Off Model

FIG. 17 is a block diagram of the loan to value (LTV) cut offdetermination model environment 1700. The environment includes LTV cutoff determination model 1245. Model 1245 generates the maximum LTV ratio1275. When generating this output, the model 1245 consider severalfactors. These factors include risk 1710, product type 1720, vehiclecondition 1730, purchase type 1740, and collateral value 1750. However,the model 1245 could consider more, less, and/or different factors thanthose displayed. Also, the model 1245 may output different and/or moreoutputs than those displayed.

FIG. 18A displays vehicle loans LTV cut off table 1800. Table 1800 canbe used to determine the maximum LTV cut off associated with anapplicant with a particular FICO score getting a loan for a type ofvehicle with a particular collateral value. Vehicle information, alongwith the type of purchase, may also be needed to determine the LTV cutoff based on table 1800. Table 1800 considers various factors byincluding columns for product type, vehicle condition, purchase type,FICO risk segment, and high or low collateral values. The correspondingLTV cut offs for each row are shown in the low and high collateralcolumns. In some embodiments, the table may include more, less, and/ordifferent criteria (columns) than those displayed. In some embodiments,the table 1800 may include more, less, and/or different rows than thosedisplayed. For example, the table may include LTV cut off ratios formotorcycle vehicle loans. Alternatively, the table 1800 may include LTVcut off rows for used recreational vehicles and new recreationalvehicles.

FIG. 18B shows vehicle loans LTV average collateral value table 1810.FIG. 18C displays the vehicle loans LTV average charge off amount table1820. Table 1810 displays the average collateral values for vehiclesbased on the product type, vehicle condition, purchase type, the FICOrisk segment of the applicants, and collateral value, as evidenced bythe columns of the table. The corresponding average collateral valuesfor each row are shown in the low collateral and high collateralcolumns. Similarly, table 1820 displays the average charge off amountsfor a vehicle loan based on the product type, vehicle condition,purchase type, the FICO risk segment of the applicants, and collateralvalue, as can be seen by the columns of the table. Also, thecorresponding average charge-off amounts for each row are shown in thelow and high collateral columns. In some embodiments, the tables 1810and 1820 may include more, less, and/or different criteria (columns)than those displayed. In some embodiments, the tables 1810 and 1820 mayinclude more, less, and/or different rows than those displayed. Forexample, the tables 1810 and 1820 may include average collateral valuesand average charge-off amounts, respectively, for motorcycle vehicleloans. Alternatively, the tables 1810 and 1820 may include averagecollateral values and average charge-off amounts, respectively, for bothused boats and new boats.

Table 1810 shows that for a given row, a substantial difference existsbetween the average collateral values for high collateral applicationsversus low collateral applications. For example, a vehicle loanapplication to refinance a used car from an applicant with a low riskFICO score has an average collateral value $11,225 for a low collateralapplication. However, a high collateral application for this row has anaverage collateral value of $23,072, more than double the averagecollateral value for a low collateral application.

Table 1820 shows that in general, the average charge-off amount for eachrow is substantially higher for a high collateral application than for alow collateral application. For example, a vehicle loan application torefinance a used car from an applicant with a low risk FICO score has anaverage charge-off amount of $7010 for a low collateral application.However, a high collateral application for this row has an averagecharge-off amount of $14,073, more than double the average charge-offamount for a low collateral application. This is true despite thecharge-off rates for high collateral applications and low collateralapplications being similar. Because the charge-off rates for high andlow collateral applications for a row are similar, the substantial jumpin the average charge-off amount between low collateral and highcollateral applications can be attributed to the large difference incollateral value between low collateral and high collateralapplications.

LTV Cut Off Determination

FIG. 20A displays used car, low risk segment graph environment 2000.Environment 2000 includes low collateral, refinance graph 2001, highcollateral, refinance graph 2002, low collateral, dealer purchase graph2003, and high collateral, dealer purchase graph 2004. FIG. 20B showsused car, medium risk segment graph environment 2010. Environment 2010includes low collateral, refinance graph 2011, high collateral,refinance graph 2012, low collateral dealer purchase graph 2013, andhigh collateral dealer purchase graph 2014. FIG. 20C provides used car,high risk segment graph environment 2020. Environment 2020 includes lowcollateral, refinance graph 2021, high collateral, refinance graph 2022,low collateral, dealer purchase graph 2023, and high collateral, dealerpurchase graph 2024. Each graph (2001-2004, 2011-2014, and 2021-2024)includes an LTV cut off line associated with that graph (2005-2008,20015-2018, and 2025-2028). The cut off lines (2005-2008, 2015-2018, and2025-2028) specify the LTV cut off for the risk segment for a type ofloan.

The graphs 2001-2004, 2011-2014, and 2021-2024 include an LTV cut offhorizontal axis 2030, a negative outcome (#Bad Rate) and charge off rate($CO Rate) vertical axis 2035, and a capture rate vertical axis 2040 (%Segment Captured). Also, graphs 2001-2004, 2011-2014, and 2021-2024 plota cumulative percentage of loans captured line 2043 (% Loans Captured),a cumulative negative outcome rate line 2041 (#Bad Rate), and acumulative charge-off rate line 2042 ($CO Rate).

In some embodiments, the graphs 2001-2004, 2011-2014, and 2021-2024include more, less, and/or different axes and lines than thosedisplayed. In some embodiments, the axes displayed contain groupingsand/or divisions more, less, and/or different than those displayed. Forexample, axis 2030 may be grouped for 10, 15, or 20 percentage points,as opposed to the displayed five percentage points. Additionally, axis2035 may have divisions of every 0.25%, instead of the displayed 0.1% or0.2% in FIG. 20A. Further, axis 2040 may show divisions of 10%, asopposed to the displayed divisions of 20%. Also, the axes may bedisplayed in different units than those shown.

When determining appropriate LTV cut offs for each risk segment, theorganization 101 may set values to generate additional vehicle loanbusiness while reducing risk. As a result, the organization 101 mayadopt a general strategy to set larger LTV cut offs for lower risksegments and smaller LTV cut offs for higher risk segments. By setting alarge maximum LTV cut offs for a low risk segment, the organization 101improves its chances to secure vehicle loan business from the lowestrisk applicants by providing them flexibility with the loan amount. Asthe risk of the applicant increases, the organization 101 may reduce theLTV cut off. By doing so, the loan amount options available to theriskier applicant are reduced. As a result, the riskier applicant isless likely to take a vehicle loan with the organization 101. This helpslimit the risk absorbed by the organization 101. FIGS. 20A-20C provideexamples of how this strategy is pursued by an organization 101 via theLTV cut off model 1245. In other embodiments, other strategies may beadopted by an organization to improve vehicle loan generation whilereducing risk.

In FIGS. 20A-20C, the organization 101 focuses on setting LTV cut offsto limit charge-off rates for each risk segment. Also, the organization101 attempts to set an LTV cut off that attracts as many potentialvehicle loan customers as possible for that risk segment while balancingthe charge-off rate. With this in mind, several general strategies canbe seen in FIGS. 20A-C to minimize the charge-off rate.

First, LTV cut offs tend to be reduced when moving from a low collateralto a high collateral loan within the same risk segment, purchase type,vehicle condition, and product. Because the negative outcome rate tendsto be similar for loans of a particular segment, purchase type, product,and vehicle condition, the value of the collateral has a major effect onthe charge-off rate. Thus, high collateral loans tend to have a highercharge-off rate than the low collateral loan. As a result, the LTV cutoff is reduced for high collateral loans.

Also, new car loans tend to receive higher LTV cut offs than used carloans. For example, for dealer purchased, new vehicles, the LTV cut offratios for all risk segments and collateral values were higher than thecorresponding risk segments and collateral values for used, dealerpurchased vehicles. Additionally, refinanced vehicle loans generallyreceive higher LTV cut offs than dealer purchased loans. For example,for used, refinanced car loans, all risk segments and collateral valueshad LTV cut offs larger than the LTV cut offs for their correspondingrisk segments and collateral values for used, dealer purchased carloans. Additionally, the LTV cut offs decrease for riskier applicantsfor the same type of vehicle loan. For example, low collateral vehicleloans for RVs and boats have LTV cut offs that reduce from a lower risksegment to a higher risk segment. Specifically, the low risk segment forthis loan has an LTV cut off (180%) that is lower than the medium risksegment value (130%), which is lower than the high risk segment value(100%).

FIG. 20A shows low risk segment graph environment 2000, which includesgraphs 2001-2004. By looking at the graphs and their associated LTV cutoff lines (2005, 2006, 2007, and 2008), the general strategy applied forthese LTV cut offs is to maximize the potential number of applicantsbecause the charge-off rates for the low risk segments are low. Forexample, every LTV cut off line (2005-2008) intersects a loan capturedline 2043 above 90%, with nearly 100% of loans being captured for allloans in the low risk segment. Additionally, charge-off rates (see line2043) for the low risk segment range from 0.15% to 0.63%. Thus, even thehighest charge-off rate in the low risk segment, 0.63%, is a relativelylow charge-off rate when compared to all applications. Thus, the generalstrategy applied to the low risk segment is to maximize the number ofapplicants.

FIG. 20B presents medium risk segment graph environment 2010, whichincludes graphs 2011-2014. For these graphs and associated LTV cut offlines (2015, 2016, 2017, and 2018), refinanced vehicles apply thegeneral strategy of maximizing the number of loan applicants whiledealer purchased vehicles set LTV cut offs to limit escalatingcharge-off rates. Refinanced, used vehicles in the medium risk segmenttend to have a relatively low charge-off rate (less than 0.6% at allcollateral values, see FIG. 19A). Thus, the LTV cut off lines 2015 and2016 are set to maximize the loans captured. Specifically, both lines2015 and 2016 intersect the loan captured line 2043 at well above 85% ofloans captured. However, for dealer purchased used vehicles in themedium risk segment, the charge-off rate, as seen in FIG. 19A, issubstantially higher at roughly 1.5%. Thus, LTV cut off lines 2017 and2018 are set at roughly 120% and 110%, respectively, to limit thecharge-off rates to approximately 0.9% and 0.7%, respectively. However,by doing so, the loans captured are significantly reduced to roughly 60%for both lines 2017 and 2018. Thus, for dealer purchased, used car loansfor medium risk segment applicants, LTV cut offs are set to reduce therisk absorbed by the organization at the expense of reducing thepotential vehicle loan applicants.

FIG. 20C displays high risk segment graph environment 2020, which hasgraphs 2021-2024. The LTV cut off lines (2025-2028) set for these graphsare meant to limit escalating charge-off rates (see line 2042) andreduce the number of high risk segment applicants receiving loans. Forexample, the loans captured percentage for the LTV cut off lines2025-2028 ranges from roughly 20%-65%. This is in stark contrast to thelow risk segment, which had LTV cut off lines 2005-2008 with loanscaptured percentages above 90%. The number of applicants is limitedbecause of the large charge-off rates associated with the high risksegment (0.97%-2.73%, according to FIG. 19A). Also, in addition toreducing the number of high-risk applicants, setting LTV cut off lines2025-2028 to lower LTV values (80-125%, see FIGS. 18A, 20C) reduces thecharge-off rates incurred by organization 101 for the high risk segment.In FIG. 20C, the charge-off rates at LTV cut off lines 2025-2028 rangefrom 0.50% (line 2027 intersecting with line 2042 for graph 2023) to1.25% (line 2028 intersecting with line 2042 for graph 2024). Thus,lower LTV cut offs for the high risk segment reduces risk fororganization 101 by lowering the number of high risk vehicle loanapplicants and limiting charge-off rates.

While FIGS. 20A-20C demonstrate for used car vehicle loans how astrategy to generate additional loan business while reducing risk canaffect the LTV cut offs for different risk segments, similar results andstrategic decisions can also be seen when analyzing collected data forother products. This includes used cars, recreational vehicles, boats,and/or other products. This may also include dealer purchased vehicles,refinanced vehicles, high collateral and/or low collateral loans, and/orother vehicles.

Also, results similar to those displayed in FIGS. 20A-20C may be seenfor risk segments different than those displayed. For example, a veryhigh risk segment 1359, and/or a very low risk segment, may also haveassociated vehicle loan data that can be charted into a graph. The graphdata may also provide insight as to the effects of different LTV cutoffs on the amount of business generated (via loans captured) and/or theamount of risk absorbed by the organization (via negative outcome ratesand charge-off rates).

Furthermore, as discussed earlier, the risk segmentation and dataanalysis displayed in FIGS. 19A-20C are based on data collectedregarding vehicle loans that were funded over an 18 month period. Insome embodiments, new data may be collected and analyzed to update therisk segmentation and LTV cut off determination from model 1245. As aresult, the LTV cut off determination model 1245 can be adjusted inresponse to updated data requiring new risk segmentation and LTV cut offdetermination, altered tolerance for risk by an organization 101,modified requirements for vehicle loan generation by an organization101, and/or other events or changes.

LTV Cut Off Examples

Similar to the term determination model examples mentioned above, in oneexample, John Doe (applicant 150) submits an application to obtain avehicle loan to refinance his used automobile, which has a collateralvalue of $20,000. John Doe's FICO score is determined to be 810. JohnDoe's application is approved for underwriting and the credit limitassignment model 230 is called. The model 230 calls LTV cut offdetermination model 1245 to determine the maximum LTV ratio 1275. Themodel 1245 determines the LTV cut off in accordance with table 1800.According to FIG. 19A, the collateral value ($20,000) is in line with ahigh collateral application, while John Doe's FICO score (810)corresponds with the low risk segment. Thus, FIG. 18A shows that JohnDoe's low risk, high collateral vehicle loan application to refinancehis used car has an LTV cut off of 180%.

In another example, John Doe has a FICO score of 750 and seeks a vehicleloan for a boat with a collateral value of $15,000. Based on FIG. 19C,John Doe has a low collateral application and falls in the medium risksegment, due to his FICO score. As a result, table 1800 dictates thatJohn Doe's medium risk, low collateral, boat purchase loan applicationhas an LTV cut off of 130%.

In yet another example, John Doe has a FICO score of 700 and needs aloan for a motorcycle (Other product type) with a collateral value of$7000. Based on FIG. 19D, John Doe has a low collateral application thatfalls into the high risk segment due to his FICO score. Consequently,table 1800 dictates that John Doe's low collateral, high risk motorcyclepurchase loan application has an LTV cut off of 140%.

In another example, John Doe has a FICO score of 810 and wants a loanfor purchasing a new car with a collateral value of $30,000. Based onFIG. 19B, John Doe's FICO score of 810 places him in the low risksegment for a vehicle loan application for a new car purchase. In turn,table 1800 shows that John Doe's loan for a new car purchase should havean LTV cut off of 180%. In the case of a new car, the value of thecollateral is not considered when determining the LTV cut off.

Payment Capacity Model

FIG. 21 is a block diagram of the payment capacity model environment2100. The environment includes payment capacity model 1250. The model1250 generates the maximum payment capacity 1280. When generating thisoutput, the model 1250 considers several factors. These factors includeapplicant income 2110, pre-loan debt to income (DTI) ratio 2120, postloan DTI ratio 2130, FICO score 2140, and acquisition score 410.Although the displayed embodiment only shows the factors mentionedabove, in other embodiments, the model 1250 may consider more, less,and/or different factors than those displayed. Also, the model 1250 mayoutput different and/or more outputs than those displayed.

FIG. 22 displays one embodiment of a maximum payment capacitycalculation environment 2200. The environment 2200 includes equation2210 and chart 2220. The chart 2220 includes a lower axis 2230 and anupper axis 2240. The equation 2210 indicates that the post loan DTIratio 2130 is a function of the acquisition score 410. In other words,the post loan DTI ratio is determined for an applicant based on theapplicant's acquisition score 410.

Chart 2220 shows one example of a function for determining the post loanDTI ratio based on the acquisition score. The bottom axis 2230 indicatesthe applicant's acquisition score while the top axis 2240 indicates theapplicant's post loan DTI ratio. In the displayed embodiment, theapplicant's acquisition score ranges from 1639 (1^(st) percentile) to2010 (99th percentile). Although not displayed, the acquisition scorescorresponding to the 25^(th), 50^(th), and 75^(th) percentiles would be1757, 1875, and 1992, respectively.

The top axis 2240 shows that the 1^(st) percentile of applicantsreported a ratio of 5%, the 50^(th) percentile of applicants reported aratio of 30%, and the 99^(th) percentile of applicants reported a 70%ratio. Although not shown, applicants in the 25^(th) percentile reportedthe corresponding post loan DTI ratio 17.5% while applicants and 75^(th)percentile reported a post loan DTI ratio of 50%. As can be seen inchart 2220, the linear relationship between the acquisition score in thepost loan DTI ratio across applicants in the bottom 50% of acquisitionscores is different from the linear relationship exhibited forapplicants in the top 50% of acquisition scores. Thus, using chart 2220,the post loan DTI ratio for an applicant can be assigned based on theapplicant's acquisition score. For example, an applicant with anacquisition score of 1757 (25^(th) percentile) would have reported apost loan DTI ratio of 17.5%. Additionally, an applicant with anacquisition score of 1875 (50^(th) percentile) would have reported apost loan DTI ratio 30%. Furthermore, an applicant with the acquisitionscore of 1992 would have reported a post loan DTI ratio of 50% (75^(th)percentile).

Although Chart 2220 demonstrates one possible function to determine apost loan DTI ratio based off an acquisition score, in otherembodiments, other equations are possible. These equations can includelinear, exponential, step, mapping, and/or other types of functionsrelating an acquisition score to a post loan DTI ratio. In someembodiments, more, less, and/or different variables from the acquisitionscore are used to determine the post loan DTI ratio.

FIG. 23 shows another embodiment of a maximum payment capacitycalculation environment 2300. In this embodiment, an applicant's FICOscore is used to determine what post loan DTI ratio cut off theapplicant should receive. Environment 2300 includes chart 2310, equation2340, and example calculation 2350. Chart 2310 shows how an applicant'spost loan DTI ratio cut off can be determined based on the applicant'sFICO score. The chart 2310 includes horizontal axis FICO scale 2320 andvertical axis DTI scale 2330. The chart plots appropriate post loan DTIcut offs for an applicant's FICO score as a step function. For example,if the applicant has a FICO score below 720, the applicant's post loanDTI ratio cut off is 48%. Alternatively, if the applicant's FICO scoreis between 755 and 800, chart 2310 shows that the applicant's post loanDTI ratio cut off is 57%. In other embodiments, other equations, such aslinear, exponential, and/or other types of functions are possible torelate an applicant's FICO score to an appropriate post loan DTI ratiocut off for the applicant.

Environment 2300 also includes equation 2340. Equation 2340 explains howto determine an applicant's maximum payment capacity based on thedetermined post loan DTI ratio cut off. As described in FIGS. 22 and 23,multiple ways are possible for determining the applicant's post loan DTIratio cut off. Either of these embodiments, or another embodiment, canbe used to determine the post loan DTI ratio cut off for an applicant.Once the cut off is determined, the cut off can be entered into equation2340 to determine the applicant's maximum payment capacity.

Example calculation 2350 shows how financial information about theapplicant is used to calculate the applicant's maximum monthly paymentcapacity. In the example calculation 2350, the applicant has a FICOscore of 700, a monthly income of $3000, and pre-loan debt commitmentsof $1000. First, the applicant's pre-loan DTI ratio is calculated bydividing the applicant's pre-loan debt commitments of $1000 by theapplicant's monthly income of $3000, resulting in a pre-loan DTI ratioof 33%. Next, using chart 2310, the applicant's FICO score of 700results in a post loan DTI ratio cut off of 48%. From this, a maximummonthly payment capacity for the applicant can be determined by takingthe difference between the post loan and pre-loan DTI ratios (48%−33%equals 15%) and multiplying this by the monthly income ($3000 multipliedby 15% equals $450) which results in a maximum monthly payment capacityof $450 for the applicant. In other embodiments, the payment capacitycan be determined for a different, applicable period of time, such as ayearly, semi-annually, and/or weekly payment capacity.

Prequalification Model

The vehicle loan generation system 200 also includes theprequalification model 240. The model 240 determines if an applicant 150is qualified to receive a vehicle loan offer. If so, the model 240generates a prequalified offer for the applicant 150. The goal of theprequalified offer is to generate additional vehicle loan business bymeeting the needs of the applicant 150 and reducing the risk taken byorganization 101. However, these goals are often in tension.Nonetheless, by providing personalized, prequalification offers in linewith limits determined by models 220 and 230, the model 240 attempts togenerate additional vehicle loan business without having theorganization absorb too much risk.

FIG. 24 is a block diagram of a prequalification model environment 2400.In environment 2400, the prequalification model 240 receives inputs 2405and generates outputs 2425. Model 240 includes the payment capacityestimator 2410, collateral estimation model 2420, front end criteria2700, and fatal criteria 2750. These models and criteria are used toprocess inputs 2405 to generate outputs 2425. The inputs 2405 receivedby model 240 include vehicle information 1210, credit bureau data 1220,loan information 1230, an acquisition score 410, maximum term 1270,maximum LTV ratio 1275 (also called LTV cut off), maximum paymentcapacity 1280, and post loan debt to income (DTI) ratio 2130. Theoutputs 2425 generated by the model 240 include a prequalified offer2430.

The vehicle information 1210, credit bureau data 1220, and loaninformation 1230 may be more, less, and/or different than theinformation and data received as inputs by model 230. Alternatively, theinformation and data 1210, 1220, and 1230 may be the same as thatreceived by model 230. While the displayed embodiment shows acquisitionscore 410 as an input, other custom scores indicating credit worthinessof an applicant may be used. The maximum term 1270, maximum LTV ratio1275, and maximum payment capacity 1280 are outputs 1260 from model 230received as inputs 2405 by model 240. The post loan DTI ratio 2130 mayalso be received by model 240 from model 230. In some embodiments, model240 receives more, less, and/or different inputs from model 230. Theinputs 2405 may include more, less, and/or different inputs than thosedisplayed in FIG. 24.

The prequalification model 240 processes inputs 2405 to determineoutputs 2425, which include a prequalified offer 2430. The prequalifiedoffer 2430 is an initial offer that indicates to an applicant 150 howmuch financing the applicant is approved for based on the estimatedvalue of the applicant's collateral. The offer 2430 is meant toencourage the applicant 150 to respond to and seek a vehicle loan fromorganization 101. This allows organization 101 to achieve its goal ofgenerating more vehicle loan business. In some embodiments, model 240generates different and/or more outputs than those displayed in FIG. 24.

The model 240 relies on the payment capacity estimator 2410, collateralestimation model 2420, front end criteria 2700, and fatal criteria 2750to process inputs 2405 to generate outputs 2425, which include theprequalified offer 2430. In some embodiments, the model 240 includesmore, less, and/or different models, estimators, and/or criteria forprocessing inputs 2405 to generate outputs 2425.

FIG. 25 is a diagram of an example method 2500 for how model 240processes inputs 2405 to generate outputs 2425. First, the modelidentifies potential customers for prequalification (block 2505). In oneembodiment, model 240 does this by retrieving from database 103applicants who were approved during the underwriting process. Theseapplicants may include applicants that were automatically approved bymodel 220, and applicants who were manually approved during theunderwriting process by model 260. In another embodiment, model 240 onlyretrieves applicants that were automatically approved during theunderwriting process.

Next, model 240 applies front end exclusion criteria to the potentialcustomers (block 2510). After that, model 240 generates aprequalification offer for a potential customer (block 2515). In oneembodiment, model 240 processes inputs 2405 to generate the prequalifiedoffer 2430. The model 240 may rely on the collateral estimation model2410 and the payment capacity estimator 2420 to generate theprequalified offer 2430.

Following this, the model 240 sends the prequalification offer to thepotential customer (block 2520). After that, the model 240 receives aresponse from the potential customer (block 2525). Next, the model 240answers the question of whether the customer fails any of the fatalcriteria (block 2530). If the answer is “yes,” then method 2500 ends(block 2545). Otherwise, if the answer is “no,” then the model 240evaluates and validates the collateral and application information(block 2535). Following this, one or more loan offers are generated forthe applicant 150 based on the application information received (block2540). After that, the method 2500 ends (block 2545). In someembodiments, the method 2500 includes more, less, and/or different stepsthan those displayed in FIG. 25. In other embodiments, the method 2500includes the displayed steps arranged in an order that is different fromthe order shown.

FIG. 26 is a diagram of a prequalification offer environment 2600.Environment 2600 includes prequalification offer 2430. Theprequalification offer 2430 includes an applicant name 2605, anapplicant loan type 2606, a prequalified vehicle loan amount 2610, and acollateral value 2615. The offer 2430 also includes terms and conditionsincluding a maximum term 1270, a maximum LTV ratio 1275, a maximumpre-loan DTI ratio 2620, and a minimum monthly income 2625. Also,environment 2600 includes equation 2630 for determining the maximum loanamount to provide for a prequalified vehicle loan amount 2610 for aprequalification offer 2430. The maximum pre-loan DTI ratio 2620indicates the maximum ratio an applicant is allowed for the prequalifiedvehicle loan offer to remain valid. The minimum monthly income 2625indicates the minimum income the applicant must maintain for theprequalified vehicle loan offer to remain valid.

In the displayed embodiment, applicant John Doe receives aprequalification offer 2430 addressed to “Mr. John Doe” 2605 for an“Auto Loan” vehicle loan type 2606. The prequalified vehicle loan amount2610 of $30,000 is based on a collateral value 2615 of $20,000. Theoffer 2430 also includes terms and conditions, such as a maximum term1270 of 72 months, a maximum LTV ratio 1275 of 150%, a maximum pre-loanDTI ratio 2620 of 20%, and a minimum monthly income 2625 of $2000 forthe applicant 150.

The various components of the offer 2430 may be received as inputs,determined by other vehicle loan generation system models, and/ordetermined by the prequalification model 240. For example, the applicantname 2605 and vehicle loan type 2606 may be determined from inputs 2405received by the prequalification model 240, including vehicleinformation 1210, credit bureau data 1220, and/or loan performanceinformation 1230. Alternatively, the maximum term 1270 and maximum LTVratio 1275 are received from the credit limit assignment model 230.

The model 240 determines the maximum pre-loan DTI ratio 2620, theminimum monthly income 2625, the collateral value 2615, and theprequalified vehicle loan amount 2610. The maximum pre-loan DTI ratio2620 may be determined by the payment capacity estimator 2410, which isdescribed later with FIG. 28. The minimum monthly income 2625 may bedetermined based off credit bureau data 1220 received for an applicant150. This is described in further detail with FIG. 28 for the paymentcapacity estimator 2410. The collateral value 2615 is calculated by thecollateral estimation model 2420, which is described in further detailin FIGS. 29-31D.

The prequalified vehicle loan amount 2610 is calculated based onequation 2630. The amount 2610 is the minimum value between vehicle loanamounts 2635 and 2640. Amount 2635 is the product of the collateralvalue 2615 and maximum LTV ratio 1275 (LTV Cutoff). As mentionedearlier, the collateral value 2615 is determined by the collateralestimation model 2420 while the maximum LTV ratio 1275 is received as aninput from the credit limit assignment model 230.

The amount 2640 equals the present value of monthly payments occurringfor a maximum term 1270 (Max Term) at a specified interest rate 2650(“Rate”) with each payment equaling the maximum payment capacity 2645.The maximum payment capacity 2645 is determined by the payment capacityestimator 2410, as described later with FIG. 28. The maximum term 1270is an input received from the credit limit assignment model 230 whileinterest rate 2650 is specified by the model 240.

The interest rate 2650 specified by model 240 may depend on variousfactors, including the vehicle loan term, the collateral value, purchasetype, product type, loan type, geographic location of the applicant,credit worthiness of the applicant, credit data of the applicant, anacquisition score, FICO score of the applicant, economic data, theorganization's tolerance for risk, the organization's need forgenerating business, risk segmentation, and/or other factors. Theinterest rate may be a monthly or annual interest rate. Alternatively,the interest rate could apply for a different, appropriate time period.

In the displayed example of FIG. 26 for applicant John Doe, theprequalification vehicle loan amount 2610 is equal to the amount 2635from equation 2630. Specifically the amount 2635 is calculated bymultiplying the collateral value 2615 of $20,000 by the maximum LTVratio 1275 of 150%, resulting in a value of $30,000 for amount 2635.Further, John Doe's prequalification vehicle loan amount 2610 is $30,000because amount 2635 is less than term 2645.

For example, in FIG. 26, if the maximum payment capacity 2645 for JohnDoe is $1000 per month, and the interest rate 2650 is 0%, then 72monthly payments (maximum term 1270) of $1000 at 0% interest would havea present value of $72,000. Thus, the amount 2640 would be $72,000.Because the amount 2640 of $72,000 is larger than the amount 2635 of$30,000, the equation 2630 requires a prequalified vehicle loan amount2610 of no more than $30,000 for John Doe. Alternatively, if theinterest rate 2650 was larger than 0%, the term 2645 would be less than$72,000, and set as the amount 2610 for values below $30,000 (amount2635). In some embodiments, the equation 2630 includes more, fewer,and/or different variables than those displayed and/or discussed above.

The prequalification offer 2430 may indicate other information about theloan, such as vehicle condition (new or used), purchase type (dealerpurchase or refinance), and/or other loan information. The offer 2430may include more, less, and/or different terms and conditions than thosedisplayed. Alternatively, the offer 2430 may not include terms andconditions. The prequalification offer 2430 may be sent to the applicant150 via email, mail, text message, phone call, fax, social network sitepost, forwarded message, social network site microblog, conversation,and/or other methods for an organization 101 to communicate with anapplicant 150. Alternatively, a third-party entity may communicate theprequalified offer on behalf of organization 101 to the applicant 150.

Front End and Fatal Criteria

FIG. 27A displays an embodiment of the front-end criteria 2700 that maybe used by the prequalification model 240. Specifically, as discussed inFIG. 25, model 240 may apply front-end exclusion criteria 2700 (block2510) to potential customers for generating a prequalification offer2430. In general, if any of the displayed front-end criteria 2700 aretrue with regards to an applicant 150, the applicant 150 is rejectedfrom receiving a vehicle loan prequalification offer. In someembodiments, more, less, and/or different front-end criteria may be usedto exclude applicants.

FIG. 27B shows an embodiment of fatal criteria 2750 that model 240 mayuse to exclude applicants. Specifically, as discussed in FIG. 25, afterreceiving a response from a potential customer (block 2525), the model240 applies the fatal criteria 2750 (block 2530) and excludes applicantsif any one of the fatal criteria 2750 is true. In some embodiments,more, less, and/or different fatal criteria 2750 may be used to excludeapplicants.

For example, if applicant John Doe is deceased, then John Doe would notbe prequalified for a loan because his application satisfied front-endexclusion criteria 2700 SI. No. 1 (see FIG. 27A). Thus, model 240 wouldnot generate a prequalification offer for John Doe. Alternatively, ifJohn Doe had a FICO score of 670, John Doe would not be prequalified fora vehicle loan because his application satisfied front-end exclusioncriteria 2700 SI. No. 22 (see FIG. 27A). Accordingly, model 240 wouldnot generate a prequalification offer for John Doe.

For another example, if John Doe has a collection account of $1900, thenJohn Doe would no longer be pre-qualified for a loan because hisapplication satisfied fatal criteria 2750 SI. No. 1 (see FIG. 27B).Therefore, model 240 would rescind the prequalification offer for JohnDoe. In yet another example, if John Doe had an outstanding judgmentagainst him for $1900, John Doe would no longer be pre-qualified for avehicle loan because his application met fatal criteria 2750 SI. No. 4(see FIG. 27B). Accordingly, model 240 would rescind theprequalification offer for John Doe.

Payment Capacity Estimator

The payment capacity estimator 2410 is called by prequalification model240 to determine the maximum payment capacity 2645 of the applicant 150.FIG. 28 displays the payment capacity estimator environment 2800.Environment 2800 includes the payment capacity estimator 2410 andoutputs 2815. In the displayed embodiment, the estimator 2410 usesseveral factors to generate outputs 2815, including the post loan DTIratio 2130, the estimated debt 2805, and an estimated income 2810. Inthe displayed embodiment, the outputs 2815 include the maximum paymentcapacity 2645. However, the outputs 2815 could include more and/ordifferent outputs than those shown. Also, in some embodiments, theestimator 2410 relies on more, less, and/or different factors than thosedisplayed.

The estimator 2410 relies on equation 2820 to determine the maximumpayment capacity 2645. The pre-loan DTI ratio (Pre-Loan DTI) iscalculated by dividing the estimated debt 2805 with the estimated income2810. In some cases, the pre-loan DTI ratio is set to the maximumpre-loan DTI ratio 2620 for the prequalification offer 2430. Also, the“Monthly Income” of equation 2820 is determined based off estimatedincome 2810 while Post-Loan DTI cutoff is given by post loan DTI ratio2130.

For example, if an applicant John Doe has a post loan DTI ratio 2130 of55%, an estimated debt 2805 requiring a $500 per month payment, and anestimated income 2810 of $1000 per month, equation 2820 can be used todetermine the maximum payment capacity 2645 for John Doe. Specifically,the pre-loan DTI is determined by dividing the estimated debt 2805monthly payment of $500 by the estimated income 2810 monthly collectionof $1000, resulting in a pre-loan DTI ratio of 50%. Next, the differenceof the post loan DTI cut off 55% (post loan DTI ratio 2130) and pre-loanDTI ratio of 50% equals 5% (55%−50% equals 5%). This difference (5%)multiplied by the monthly income of $1000 per month (estimated income2810) results in a maximum payment capacity 2645 of $50 per month[$50=$1000*(55%−50%)].

In the displayed embodiment of FIG. 28, the post loan DTI ratio 2130 isan input received by the prequalification model 240 from the creditlimit assignment model 230. However, in other embodiments, the model 240calculates the post loan DTI ratio 2130. In the displayed embodiment ofFIG. 28, the estimated debt 2805 and estimated income 2810 aredetermined from credit bureau data 1220 received by the model 240. Insome embodiments, the estimated debt 2805 is derived from the variable“Aggregate Monthly Payment for Open Trades” while the estimated income2810 is derived from the variable “Income Insight W2.” In otherembodiments, different methods, variables, and/or data may be used todetermine the estimated debt 2805 and estimated income 2810.

In some embodiments, the estimator 2410 may rely on one or moredifferent equations and/or criteria to generate outputs 2815. Forexample, the estimator 2410 may rely on similar methods disclosed forthe payment capacity model 1250 (see FIGS. 21, 22, and 23 and theaccompanying descriptions).

Collateral Estimation Model

The collateral estimation model 2420 is called by prequalification model240 to determine the value of the collateral of the applicant 150. FIG.29 displays the collateral estimation environment 2900. Environment 2900includes collateral estimation model 2420 and outputs 2910. In thedisplayed embodiment, collateral estimation model 2420 includes apremium collateral probability estimator 3100, a high collateralprobability estimator 3110, and a low collateral probability estimator3120. The collateral estimation model 2420 uses these estimators togenerate outputs 2910. In the displayed embodiment, the outputs 2910include the collateral value 2615. In some embodiments, model 2420includes more, less, and/or different models, estimators, and/orcriteria than those displayed. In some embodiments, the outputs 2910include more, and/or different outputs than those shown.

FIG. 30A shows one embodiment of collateral segmentation table 3010. Inthe displayed embodiment, table 3010 includes three collateral segments:low collateral, high collateral, and premium collateral. Premiumcollateral is for collateral values above $50,000. High collateral isfor collateral values above $25,000 and less than or equal to $50,000.Meanwhile, low collateral is for collateral values less than or equal to$25,000. As shown in the table, nearly 55% of all applications have lowcollateral, while roughly 44% of applications have high collateral. Theremaining applications fall into the premium collateral segment.

While the displayed embodiment shows three different collateralsegments, more, less, and/or different collateral segments are possible.Additionally, different collateral value ranges could be assigned tocollateral segments than those shown. Furthermore, the number ofapplications that fall within these segments may be different than whatis shown in FIG. 30A. For example, the collateral segments could havecollateral value ranges selected to encourage a particular distributionof applications across the three segments. Specifically, collateralsegments could be defined by collateral value ranges to ensure thatroughly 33% of applications fall within each different collateralsegment, thus causing an equal distribution of the applications acrossthe three collateral segments. Alternatively, different distributions,such as an unequal distribution of applications across the collateralsegments, could be achieved with different collateral value ranges.

In the displayed embodiment, the collateral segments, values of thecollateral segments, and number of applications within those segments isbased on data collected by an organization 101, as discussed earlier forrisk segmentation for FIGS. 14A-15C and 19A-20D. Similarly, if thecollected data changes or is modified, different collateral segments maybe generated.

Model 2420 may achieve different objectives by assigning a value tocollateral. In the displayed embodiments of FIGS. 25 and 30B, when model2420 attempts to assign a value to collateral, the model 2420 is focusedon determining the probability of receiving a response from theapplicant 150 to the prequalification offer (see block 2525 from FIG.25). The probability of this response varies based on the collateralvalue assigned. Thus, model 2420 attempts to assign an appropriatecollateral value to encourage the applicant 150 to respond to theprequalification offer (block 2525 from FIG. 25). However, in someinstances, the collateral estimation model 2420 may be designed toestimate collateral to achieve a different purpose. For example, themodel 2420 may estimate collateral to approximate fair market value forthe collateral, regardless of the effect on the response rate of aprequalification offer.

FIG. 30B displays an example of collateral value assignment method 3020.The method 3020 may be executed by collateral estimation model 2420.First, model 2420 determines premium collateral response probability(block 3025). In this case, model 2420 may call premium collateralprobability estimator 3100 (see FIGS. 29A, 31A) to execute blocks 3025.Next, model 2420 then answers the question of whether the collateral ispremium collateral (block 3030). In this embodiment, the model 2420answers the question “yes” when the determined premium collateralresponse probability is greater than or equal to a premium collateralresponse probability threshold, which in this case, is 7.9% (notdisplayed). In some embodiments, a higher (or lower) thresholdpercentage may be used than 7.9%. If the answer to the premiumcollateral question (block 3030) is yes, then the method 3020 proceedsto step 3060. Here, model 2420 assigns the premium collateral a value of$50,000 (block 3060). After that, the method 3020 ends (block 3065).

If the answer to the question (block 3030) is “no,” the method 3020 thenproceeds to block 3035. Next, the method 3020 determines the highcollateral response probability (block 3035). Specifically, collateralestimation model 2420 calls high collateral probability estimator 3110.Afterwards, method 3020 determines the low collateral responseprobability (block 3040). Specifically, model 2420 calls the lowcollateral probability estimator 3120. Once determined, method 3020 thenanswers the question of whether the low collateral response probabilityis greater than the high collateral response probability (block 3045).If the answer is “no,” the collateral is high collateral, and method3020 proceeds to block 3055. Here, the method 3020 assigns thecollateral a value based on a high collateral estimator table (see FIG.30D) (block 3055). After that, the method ends (block 3065).

Alternatively, if the answer is “yes,” the collateral is low collateral,and the method 3020 assigns the collateral a value in accordance withthe low collateral estimator table (see FIG. 30C) (block 3050). Next,the method 3020 ends (block 3065).

While the described method 3020 determines a collateral segment(premium, high, or low) by comparing response probabilities, otherfactors may be considered for determining collateral segments. Forexample, a collateral value could be analyzed, with certain valuescorresponding to a premium, high, low, or other collateral segment. Insome embodiments, the method 3020 includes more, less, and/or differentsteps than those displayed in FIG. 30B. In other embodiments, the method3020 includes the displayed steps arranged in a different order from theorder shown.

FIG. 30C shows the low collateral estimator table 3051 (see block 3050from FIG. 30B). Also, FIG. 30D shows the high collateral estimator table3056 (see block 3055 FIG. 30B). For low collateral, table 3051 assigns acollateral value equal to the average collateral value based on the lowcollateral probability response determined (see block 3040). Forexample, if the low collateral had a low collateral probability responseof 61%, the low collateral receives a value of $24,900, according totable 3051. Alternatively, if the low collateral had a low collateralprobability response of 89%, the low collateral is assigned a value of$21,400. Once the low collateral receives a value, the collateralestimation model 2420 can generate a collateral value output 2615 equalto the assigned value.

For high collateral, table 3056 sets a collateral value equal to theaverage collateral value that corresponds with a high collateralprobability response determined (see block 3035). For example, if thehigh collateral had a high collateral probability of 88%, the assignedvalue for high collateral would be $33,100, according to table 3056.Alternatively, if the high collateral a high collateral probabilityresponse of 47%, the high collateral would receive a value of $26,800,according to table 3056. After the high collateral receives a value, thecollateral estimation model 2420 can create a collateral value output2615 equal to the assigned value. For premium collateral, in thedisplayed embodiment, the model 2420 automatically assigns a value of$50,000 and generates an equivalent collateral value output 2615.

Although the displayed embodiments of FIGS. 30C and 30D assign theaverage collateral value for applications with a correspondingprobability, in some embodiments, a larger, smaller, and/or differentcollateral value may be assigned than those displayed. For example,assigned collateral values could be the average collateral value plus orminus an offset (for example, $500). Additionally, in some embodiments,the probability ranges may be smaller, larger, and/or different thanthose displayed in FIGS. 30C and 30D. Also, while FIGS. 30C and 30Ddisplay 10 groups of probability ranges, it's possible that more or lessprobability ranges could be used. Furthermore, while the displayedembodiment for FIGS. 30A-30D assign one value to premium collateral($50,000), in other embodiments, premium collateral could be assignedmultiple values. For example, similar to what is shown in FIGS. 30C and30D, multiple collateral values could be assigned based on the premiumcollateral probability response.

FIG. 31A displays an embodiment of the premium collateral probabilityestimator 3100. The estimator 3100 analyzes the data associated with anapplicant 150 to determine the probability of the applicant respondingto a prequalification offer that categorizes the collateral as premiumcollateral (premium collateral probability response). FIG. 31B shows anembodiment of the high collateral probability estimator 3110. Estimator3110 processes the data associated with an applicant 150 to calculatethe probability of the applicants responding to a prequalification offerthat categorizes his collateral as high collateral (high collateralprobability response). FIG. 31C provides an embodiment of the lowcollateral probability estimator 3120. The estimator 3120 uses the dataassociated with an applicant 150 to figure out the probability of theapplicant responding to a prequalification offer that categorizes thecollateral of the applicant as low collateral (low collateralprobability response).

Each estimator displayed in FIGS. 31A-C includes multiple variables, adescription for each variable, and a contribution percentage for eachvariable towards the probability response calculated by thecorresponding probability estimator. The contribution percentagerepresents the numerical weight to assign to the variable whencalculating the premium collateral probability response using theestimator 3100. Similar to FIG. 6, while the displayed embodiments ofFIGS. 31A-C show different contribution values for each variable (e.g.,“A %”, “B %”, etc.), in some embodiments, two or more variables may havethe same contribution value (e.g., multiple variables in FIG. 31A havinga contribution value of “A %”).

Each estimator (3100, 3110, and 3120) also includes a variable calledintercept. The intercept variable allows an offset to be included in theprobability response calculation, if necessary. The intercept variablesare independent for each estimator. That is, the intercept variable forthe estimator 3100 may be different from the intercept variable for theestimator 3110, which may be different from the intercept variable forthe estimator 3120. While FIGS. 31A-C display an embodiment of eachestimator, in other embodiments, each estimator may have more, less,and/or different variables than those displayed. Additionally, eachestimator may have higher, lower, and/or different contributionsassigned to each variable than those displayed.

FIG. 31D shows key coded estimator variable table 3130 for the key codedmonthly payment for the oldest open auto trade variable. This variableis used by estimators 3100, 3110, and 3120. While the displayedembodiment 3130 shows 12 different key codes, in other embodiments,more, less, and/or different key codes than those displayed arepossible. Additionally, the monthly payment amount ranges may be larger,smaller, and/or different than those displayed in FIG. 31D. Also, inother embodiments, the variable monthly payment for oldest open autotrade may not be key coded. For FIGS. 31A-31D, estimators 3100, 3110,and 3120 may include more, less, and/or different key coded variablesthan those displayed.

In the displayed embodiment of FIGS. 29-31D, the collateral segments(premium collateral, high collateral, and low collateral) weredetermined by analyzing vehicle loans for new cars over a determinedtime period. In the displayed embodiment, the time period is 18 months.However a different time period, such as one year, two years, or threeyears, could be used. The applications were analyzed to determinevarious statistics about the performance of the loans, including vehiclecollaterals, response to prequalification offer probabilities, financialdata about the applicant, and/or other data and statistics relevant tothe vehicle loan and the applicant. Although the estimators 3100, 3110,and 3120 (see FIGS. 31A-31D) were developed using logistic regressionanalysis, other statistical methods could be used to develop models topredict probability responses for different collateral segments based onvarious financial, loan performance, credit, and/or other data.

Collateral Estimation Model Example

In one example, the prequalification model 240 receives inputs and dataabout the auto loan application from applicant John Doe. The model 240calls collateral estimation model 2420 to determine the value of thecollateral associated with John Doe's application. The collateralestimation model 2420 executes method 3020 (see FIG. 30B) to determinethe value of the collateral.

The model 2420 calls the premium collateral probability estimator 3100to determine the premium collateral response probability 3025. In thisexample, the premium collateral response probability is determined to be10%. Next, model 2420 determines if the collateral is premium collateral3030 by comparing the premium collateral response probability (10%) withthe premium collateral response probability threshold (7.9%). Becausethe response probability of 10% is larger than the threshold of 7.9%,the collateral is considered premium collateral. As a result, model 2420assigns the collateral a value of $50,000 (see block 3060 in the FIG.30B). Once the collateral value is assigned, the model 2420 generatesoutputs 2910 reflecting the collateral value output 2615 as $50,000.

In another example, once the prequalification model 240 calls thecollateral estimation model 2420 to assign a collateral value, the model2420 executes method 3020. The model 2420 calls the premium collateralprobability estimator 3100 to determine the premium collateral responseprobability 3025. In this example, the premium collateral responseprobability is determined to be 5%. Afterwards, model 2420 determines ifthe collateral is premium collateral 3030 by comparing the premiumcollateral response probability 3025 (5%) with the premium collateralresponse probability threshold (7.9%). Because the response probabilityof 5% is lower than the threshold of 7.9%, the collateral is notclassified as premium collateral.

Next, the model 2420 calls the high collateral probability estimator3110 to determine the high collateral response probability 3035. Model2420 also calls the low collateral probability estimator 3120 todetermine the low collateral response probability 3040. In this example,the high collateral response probability is 55% while the low collateralresponse probability is only 45%. Because the low collateral responseprobability is less than the high collateral response probability(45%<55%), the collateral is assigned to the high collateral segment.Next, the model 2420 relies on the high collateral estimator table 3056to assign a collateral value 3055. In this case, the high collateralresponse probability of 55% translates to an average collateral value$28,300. Thus, the collateral estimation model 2420 generates outputs2910 with a collateral value output 2615 of $28,300.

In yet another example, the collateral estimation model 2420 executesmethod 3020 after being called upon by prequalification model 240. Themodel 2420 calls the premium collateral probability estimator 3100 todetermine the premium collateral response probability 3025. In thisexample, the premium collateral response probability is determined to be5%. Next, model 2420 determines if the collateral is premium collateral3030 by comparing the premium collateral response probability (5%) withthe premium collateral response probability threshold (7.9%). Becausethe response probability of 5% is lower than the threshold of 7.9%, thecollateral is not considered premium collateral.

Next, the model 2420 calls the high collateral probability estimator3110 to determine the high collateral response probability 3035. Model2420 also calls the low collateral probability estimator 3120 todetermine the low collateral response probability 3040. In this example,the high collateral response probability 3035 is 45% while the lowcollateral response probability 3040 is 55%. Because the low collateralresponse probability 3040 is greater than the high collateral responseprobability 3035 (55%>45%), the collateral is assigned to the lowcollateral segment. Next, the model 2420 relies on the low collateralestimator table 3051 to assign a collateral value 3050. In this case,the low collateral response probability 3040 of 55% translates to anaverage collateral value of $25,000. Thus, the collateral estimationmodel 2420 generates outputs 2910 with a collateral value output 2920 of$25,000.

In some embodiments, the high collateral response probability and lowcollateral response probability are dependent variables, meaning the sumof the variables must be less than or equal to 100%. In otherembodiments, the high collateral response probability and low collateralresponse probability are independent variables. In these scenarios, thesum of the high collateral response probability and low collateralresponse probability may be less than, greater than, or equal to 100%.

Multiple Offers Model

The vehicle loan generation system 200 also includes the multiple offersmodel 250. The multiple offers model 250 generates multiple offers foran applicant 150. Additionally, the multiple offers model 250 customizesoffers for an applicant 150. When generating multiple offers, the model250 relies on previously calculated metrics, such as maximum terms,amounts, and LTV ratios. Based on these previously calculated values,additional credit data, vehicle information, applicant information, andvarious business rules, the multiple offers model 250 generates multipleoffers for an applicant 150. The vehicle loan offers will differ withregards to the term, the monthly payments, the loan amount, and theinterest rate. By generating multiple offers for the vehicle loangeneration system 200, an applicant 150 is able to choose the vehicleloan offer that best suits him. Further, the model 250 may allow theapplicant 150 to enter his own chosen parameters to generate acustomized vehicle loan offer.

FIG. 32 is a block diagram of a multiple offers model environment 3200.Model 250 receives inputs 3205 and generates outputs 3225. Model 250includes the offer generation model 3210 and the offer customizationmodel 3220. Models 3210 and 3220 are used by model 250 to process inputs3205 to generate outputs 3225. The inputs 3205 received by model 250include vehicle information 1210, credit bureau data 1220, maximum term1270, maximum LTV ratio 1275 (also called LTV cutoff), maximum paymentcapacity 1280, post loan debt to income (DTI) ratio 2130, and custominputs 3206. The outputs 3225 generated by the model 250 include a firstoffer 3230, a second offer 3235, a third offer 3240, and a recalculatedoffer 3245. In some embodiments, the model 250 includes more, less,and/or different models or criteria for processing inputs 3205 togenerate outputs 3225.

The vehicle information 1210 and credit bureau data 1220 may be more,less, and/or different than the information and data received as inputsby models 230 and 240. Alternatively, the information and data 1210 and1220 may be the same as that received by models 230 and 240. The maximumterm 1270 and maximum LTV ratio 1275 are outputs 1260 from model 230received as inputs 3205 by model 250. The post loan DTI ratio 2130 mayalso be received by model 250 from model 230. In some embodiments, model250 receives more, less, and/or different inputs from model 230. Theinputs 3206 are custom inputs received from an applicant 150. The custominputs 3206 may be used by offer customization model 3220. The inputs3205 may include more, less, and/or different inputs than thosedisplayed in FIG. 32.

The multiple offers model 250 generates outputs 3225, which include afirst vehicle loan offer 3230, a second vehicle loan offer 3235, a thirdvehicle loan offer 3240, and a recalculated vehicle loan offer 3245. Thefirst offer 3230 is a vehicle loan offer generated by offer generationmodel 3210 for applicant 150 based on inputs 3205. The offer 3230 is forthe maximum term. The second offer 3235 is a vehicle loan offer forapplicant 150 with a term that is 12 months less than the maximum term.The third offer 3240 is a vehicle loan offer generated for applicant 150with a term that is 24 months less than the maximum term. Therecalculated offer 3245 is a vehicle loan offer for applicant 150generated by offer customization model 3220 based on inputs 3205,including custom inputs 3206. In some embodiments, the model 250generates more, less, and/or different outputs than those shown in FIG.32.

The first, second, and third offers 3230, 3235, and 3240, respectively,are meant to provide the applicant 150 different options for a vehicleloan offer. Additionally, the recalculated offer 3245 provides theapplicant 150 an opportunity to customize an offer based on theapplicant's preferences. By providing the applicant multiple choices andcustomization opportunities, the organization 101 is able to achieve itsgoal of generating more vehicle loan business.

Model Inputs

FIG. 33 is a snapshot of a product type inputs interface 3300. Theinterface 3300 includes a title 3310, an automobile selection button3320, an RVs/Boats selection button 3325, and an other productsselection button 3330. Selection of any one of the buttons 3320, 3325,and/or 3330 provides the multiple offers model 250 with the product typeinput. For example, selecting the RVs/Boats button 3325 designates theproduct type as a vehicle loan for either a recreational vehicle or aboat. Alternatively, selecting the button 3320 would set the producttype as a vehicle loan offer for a car. For another example, the button3330 may be selected for a vehicle loan for a motorcycle. The interface3300 also includes an exit selection button 3335. Selection of button3335 allows the user to exit the interface 3300. In some embodiments,the interface 3300 includes more, less, and/or different buttons and/orcomponents than those shown in FIG. 33.

FIG. 34 displays a snapshot of a multiple offers model inputs interface3400. The interface 3400 includes input titles 3410, input entry boxes3420, a clear selection button 3430, a submit information selectionbutton 3435, and a close selection button 3440. Selection of the clearselection button 3430 causes the input entry boxes 3420 to be clearedand/or reset to a default value. Selection of the submitted informationbutton 3435 causes the information entered in the boxes 3420 to besubmitted to the multiple offers model 250 for processing. Selection ofthe button 3440 causes the interface 3400 to close.

In the displayed embodiment of FIG. 34, the interface 3400 includesseveral inputs 3410. These inputs include the applicant's socialsecurity number, resident state, occupation, monthly net income, debtpayments (monthly), purchase type, collateral value, vehiclemake/manufacturer, vehicle model year, and vehicle mileage. In someembodiments, the applicant's social security number is used to obtainthe applicant's FICO score from a credit bureau. Applicant's residentstate may affect the terms of the pricing model used by the offergeneration model 3210, as will be discussed later. The applicant'smonthly income and debt payments may be used to determine theapplicant's monthly vehicle loan payment capacity. The applicant'soccupation status may be relevant with respect to different policyguidelines followed by the offer generation model 3210.

As for the vehicle information, the product type and purchase type mayaffect the maximum term, LTV, and the pricing determined by the offergeneration model 3210. The vehicle mileage and/or model year mayindicate whether the vehicle is new or used, which may then affectpricing determined by the offer generation model 3210. Collateral valuemay be used for determining LTV and the appropriate loan amount to offerthe applicant while the vehicle make/manufacturer may be relevant withrespect to different policy guidelines that are followed by the offergeneration model 3210. The offer generation model 3210 is explainedlater in further detail.

The input entry boxes 3410 may be blank, display the previous value, ordisplay a default value when the interface 3400 is first generated. Forexample, the default value for the input “purchase type” may be dealerpurchase while the default value for the vehicle mileage input maybe 0miles. In this case, the default values would reflect a new vehiclepurchase from a dealer. However, other default values are possible.Additionally, the interface 3400 includes input entry boxes 3420 withdrop-down menus 3425. For menus 3425, multiple options may be provided.

In some embodiments, the interface 3400 includes more, less, and/ordifferent buttons and/or components than those shown in FIG. 34.Additionally, the interface 3400 may request more, less, and/ordifferent inputs than those shown in FIG. 34.

Offer Generation Model

FIG. 35 is a block diagram of the offer generation model environment3500. The environment 3500 includes the offer generation model 3210 fromthe multiple offers model 250. The offer generation model 3210 generatesoutputs 3540 which includes a vehicle loan offer 3550 for an applicant.The offer generation model 3210 relies on the inputs 3205 (not shown inFIG. 35, see FIG. 32) received by the multiple offers model 250 togenerate the outputs 3540, including offer 3550. When the model 3210processes the inputs 3205, the model relies on policy guidelines 3510,pricing model 3520, and loan amount calculation engine 3530.

The outputted offer 3550 from the offer generation model 3210 may beused by the multiple offers model 250 for providing outputs 3225. Forexample, the offer 3550 may correspond with any of the outputs 3225,such as the first offer 3230, the second offer 3235, the third offer3240, or the recalculated offered 3245. In one embodiment, the offergeneration model 3210 is run multiple times to generate all of theoutputs 3225 for the model 250. For example, for a vehicle loanapplication from an applicant, the model 250 may call the offergeneration model 3210 a first time to generate the first offer 3230, asecond time to generate the second offer 3235, and a third time togenerate the offer 3240. In the example, the first offer 3230 maycorrespond to a vehicle loan offer with a maximum term. The second offer3235 may correspond to a vehicle loan offer with a term 12 monthsshorter than the maximum term. The third offer 3240 may correspond to avehicle loan offer with a term 24 months shorter than the maximum term.Also, the model 250 may call the offer generation model 3210 a fourthtime in response to custom inputs 3206 to generate the recalculatedoffer 3245. For the recalculated offer 3245, the custom inputs 3206 mayalter the vehicle loan term, the vehicle loan amount, and/or otherconditions of the vehicle loan.

In other embodiments, the offer generation model 3210 has outputs 3540which includes more offers than just the single offer 3550 displayed. Asa result, in this embodiment, the offer generation model 3210 may not berequired to run multiple times to generate all of the offers shown inoutputs 3225 (i. e., offers 3230, 3235, 3240, and 3245). For example, inresponse to a vehicle loan application, the offer generation model 3210may output three vehicle loan offers, including a first vehicle loanoffer with a maximum term corresponding to offer 3230, a second vehicleloan offer corresponding to offer 3235 with a term 12 months shorterthan the first offer, and a third vehicle loan offer corresponding tooffer 3240 with a term 24 months shorter than the first offer.Additionally, the outputs may include a fourth vehicle loan offercorresponding to recalculated offer 3245 in response to custom inputs3206. In some embodiments, the offer generation model 3210 includesmore, less, and/or different outputs than those displayed in FIG. 35.

The offer generation model 3210 relies on policy guidelines 3510,pricing model 3520, and loan amount calculation engine 3530 to processinputs 3205 to generate outputs 3540. In some embodiments, the model3210 includes more, less, and/or different guidelines, models, engines,and/or components to process inputs to generate outputs.

Offer Generation Model: Policy Guidelines

FIG. 36 displays a block diagram of the policy guidelines environment3600. Environment 3600 includes policy guidelines 3510, inputs 3605, andoutputs 3620. Policy guidelines 3510 are used by the offer generationmodel 3210 to further process specific inputs 3205 received by themultiple offers model 250. The specific inputs that are processed areshown as inputs 3605. These inputs are processed by the model 3210 inaccordance with policy guidelines 3510 to generate outputs 3620. Theoutputs 3620 include policy maximum term 3630, policy maximumloan-to-value ratio 3640, and policy post loan debt to income ratio3650. These outputs 3620 may be affected by the received inputs 3605,including maximum term 1270, maximum loan-to-value ratio 1275, post loandebt to income ratio 2130, and amount 3610. Amount 3610 is a valuecalculated by the loan amount calculation engine 3530, which isdescribed in a later section.

FIG. 37 displays a loan to value policy guidelines table 3700 and postloan debt to income policy guidelines table 3710. The policy guidelinesoutlined in table 3700 allow the offer generation model 3210 to set apolicy maximum loan-to-value ratio 3640 based on the maximumloan-to-value ratio 1275 to comply with the policy guidelines displayedin the table 3700. Although the policy maximum loan-to-value ratio 3640can equal the loan-to-value ratio 1275, in some cases, the policymaximum loan-to-value ratio 3640 will be different from the maximumloan-to-value ratio 1275 to ensure compliance with the policyguidelines. For example, table 3700 requires all vehicle loans forrecreational vehicles or boats to have a maximum loan-to-value ratio of115%. Thus, if the multiple offers model 250 receives inputs including amaximum loan-to-value ratio 1275 of 120% for a recreational vehicle loanfor an applicant, the offer generation model 3210 will generate a policymaximum loan-to-value ratio 3640 equal to 115% to ensure compliance withpolicy guidelines 3510. As a result, the ratio 3640 would be differentfrom the inputted ratio 1275. The offer generation model 3210 would thenrely on the policy maximum loan-to-value ratio 3640 when determiningpotential vehicle loan offers to ensure compliance with policyguidelines 3510.

Similarly, the offer generation model 3210 can set a policy post loandebt to income ratio 3650 based on the maximum post loan debt to incomeratio 2130 to comply with the policy guidelines displayed in table 3710.Although the policy post loan debt to income ratio 3650 could equal thepost loan debt to income ratio 2130, in some cases, the policy post loandebt to income ratio 3650 will be different from the post loan debt toincome ratio 2130 to ensure compliance with the policy guidelines. Forexample, table 3710 requires the post loan debt to income ratio for allproducts to be no larger than 50%. Consequently, if the multiple offersmodel 250 receives inputs including a post loan debt to income ratio2130 equal to 75% for a vehicle loan, the offer generation model 3210will generate a policy post loan debt to income ratio 3650 of 50% tocomply with the policy guidelines. The policy post loan debt to incomeratio 3650 would then be relied upon by the offer generation model 3210when generating subsequent vehicle loan offers.

FIG. 38 displays a maximum term policy guidelines table 3800. The policyguidelines outlined in table 3800 allow the offer generation model 3210to set the policy maximum term 3630 based on the maximum term 1270 tocomply with the policy guidelines displayed in the table 3800. Althoughthe policy maximum term 3630 could equal the maximum term 1270, in somecases, the policy maximum term 3630 will be different from the maximumterm 1270 to ensure compliance with the policy guidelines. In thedisplayed embodiment of FIG. 38, the policy guidelines of table 3800constrain the maximum term based on a vehicle loan amount 3610. Thus,the offer generation model verifies that the generated vehicle loanoffer amount and term comply with the policy guidelines of table 3800.

For example, if offer generation model 3210 generates a vehicle loanoffer of $10,000 (amount 3610) for 84 months (maximum term 1270) for acar, the offer generation model will generate a policy maximum term 3630of 72 months or less for a loan amount of $10,000 to comply with thepolicy guidelines of table 3800. The policy maximum term 3630 of 72months or less would then be used by offer generation model 3210 forgenerating subsequent vehicle loan offers to ensure compliance withpolicy guidelines 3510.

In some embodiments, policy guidelines 3510 may include more, less,and/or different policy guidelines than those displayed or discussed inaccordance with FIGS. 36-38. Further, the guidelines 3510 may be basedon more, less, and/or different criteria (e.g., product type, vehiclemanufacturer, purchase type, vehicle condition, FICO score, etc.) thanthose displayed or discussed in accordance with FIGS. 36-38. Forexample, a policy guideline regarding maximum permissible loan to valueratios could be based on a vehicle manufacturer. Specifically, differentvehicle manufacturers may warrant a higher or lower permissible maximumloan to value ratio. As a result, policies could exist which modify themaximum permissible loan to value ratio based on the vehiclemanufacturer. In some embodiments, the policy guidelines environment3600 relies on more, less, and/or different inputs 3605 than thosedisplayed in FIG. 36. In some embodiments, the environment 3600displayed in FIG. 36 generates and/or affects more, less, and/ordifferent outputs 3620 than those displayed.

Offer Generation Model: Pricing Model

FIG. 39 displays a block diagram of the pricing model environment 3900.Environment 3900 includes pricing model 3520, inputs 3905, and outputs3930. The inputs 3905 are based on the inputs 3205 received by themultiple offers model 250 and the outputs of policy guidelines 3510. Theinputs 3905 include vehicle information 1210, credit bureau data 1220,policy maximum term 3630, policy maximum loan-to-value ratio 3640,policy post loan debt to income ratio 3650, and amount 3610. The inputsare processed by model 3520 to generate outputs 3930. Although notdisplayed, in some embodiments, the maximum term 1270, the maximumloan-to-value ratio 1275, and the post loan debt to income ratio 2130may be used instead of their corresponding policy values (i.e., policymaximum term 3630, policy maximum loan-to-value ratio 3640, and policypost loan debt to income ratio 3650).

The outputs 3930 include a pricing maximum term 3940 and a vehicle loanannual interest rate 3950. The pricing maximum term 3940 may update themaximum term 1270 and/or the policy maximum term 3630. The pricing model3520 relies on pricing constraints 3910 to generate, in this case, thepricing maximum term 3940. Additionally, the model 3520 uses the annualinterest rate determination model 3920 to generate the vehicle loanannual interest rate 3950. The generated rate 3950 is then reflected inthe offer 3550 generated by the offer generation model 3210. In someembodiments, the pricing model 3520 may include more, less, and/ordifferent inputs, outputs, models, guidelines, and/or other components.

FIG. 40 displays a pricing constraints term table 4000. The table 4000allows the pricing model 3520 to modify the policy maximum term 3630 tocomply with the pricing constraints displayed in table 4000.Specifically, the pricing model 3520 can only change the policy maximumterm 3630 by reducing it to ensure compliance with both policyguidelines 3510 and pricing constraints. Although the pricing maximumterm 3940 may be the same as policy maximum term 3630 and/or maximumterm 1270, in some cases, the pricing maximum term 3940 is differentfrom the policy maximum term 3630 and/or the maximum term 1270. Thepricing maximum term 3940 may be different from the other terms toensure compliance with pricing constraints 3910. In the displayedembodiment of FIG. 40, the table 4000 includes price constraints basedon vehicle information 1210, such as product type and model year, alongwith credit bureau data 1220 for the applicant, such as the FICO scoreof the applicant. Thus, the pricing model 3520 verifies that the vehicleloan offer term complies with pricing constraints based on the vehicleproduct type, model year, and applicant FICO score.

For example, the pricing model 3520 may receive inputs 3905 for avehicle loan including a policy maximum term 3630 of 72 months, vehicleinformation 1210 specifying a car from 2002 (product type and modelyear), and credit bureau data 1220 indicating an applicant FICO score of600. For this example, the pricing model 3520 would generate a pricingmaximum term 3940 of 36 months to comply with the pricing constraints oftable 4000. However, if the vehicle model year were 2010, instead of2002, the pricing model 3520 would instead generate a pricing maximumterm 3940 of 60 months to comply with the pricing constraints of table4000.

In yet another example, if the FICO score of the applicant for the carloan was 700, as opposed to 600, the pricing model 3520 would generate apricing maximum term 3940 of 72 months, which equals the received policymaximum term 3630. In this case, the policy maximum term 3630 of 72months not only complies with policy guidelines 3510, but it alsocomplies with the pricing constraints of table 4000. Thus, the pricingmodel 3520 does not have to select a time period different from thepolicy maximum term 3630 when setting the pricing maximum term 3940 at72 months. The pricing maximum term 3940 is then used by the offergeneration model 3210 for generating subsequent vehicle loan offers toensure compliance with pricing constraints 3910.

In some embodiments, pricing constraints 3910 may include more, less,and/or different constraints than those displayed in FIG. 40. Further,the constraints may be based on more, less, and/or different criteria(e.g., product, purchase type, vehicle condition, FICO score, etc.) thanthe criteria displayed. Additionally, the pricing constraints 3910 mayaffect more, less, and/or different outputs than just the pricingmaximum term 3940.

Pricing Model: Annual Interest Rate Determination Model

FIG. 41 displays a block diagram of the annual interest ratedetermination environment 4100. Environment 4100 includes the annualinterest rate determination model 3920, inputs 4105, and outputs 4115.The purpose of model 3920 is to provide information regardingappropriate annual interest rates for possible vehicle loans. The inputs3905 include policy maximum term 3630, policy maximum loan-to-valueratio 3640, policy post loan debt to income ratio 3650, debt to incomecapped loan amount 4110, vehicle information 1210, and credit bureaudata 1220. The debt to income capped loan amount 4110 is received fromthe loan amount calculation engine 3530, which is explained in furtherdetail later. Vehicle information 1210 includes a purchase type, avehicle type, a collateral value, a vehicle condition, a vehicle modelyear, and/or other information about the vehicle. Credit bureau data1220 may include information such as the FICO score of the applicant,state of residence of the applicant, monthly income of the applicant,pre-loan monthly debt payments of the applicant, and/or otherinformation. In some embodiments, the inputs 4105 include more, less,and/or different inputs than those displayed and/or described above.

Received inputs 4105 may be used to determine other inputs and factors.For example, credit bureau data 1220 and policy post loan debt to incomeratio 3650 may be used to calculate a maximum loan payment for anapplicant using methods similar to those described for payment capacityestimator 2410. Specifically, the maximum payment for an applicant maybe calculated by first multiplying the policy post loan debt to incomeratio 3650 by the applicant's monthly income, and subtracting thatproduct by the applicant's pre-loan monthly debt payments to determinethe maximum loan payment the applicant can afford. In other embodiments,other factors and/or inputs may be calculated based on inputs 4105.

The model 3920 processes inputs 4105 to determine outputs 4115, whichinclude an estimated annual interest rate 4120 and an annual interestrate 3950. The estimated annual interest rate 4120 may be used by theloan amount calculation engine 3530, which is described in furtherdetail later. The annual interest rate 3950 and/or the estimated annualinterest rate 4120 may be used by the offer generation model 3210 togenerate the offer 3550. Additionally, the interest rates 3950 and/or4120 may be provided with the offer 3550. In some embodiments, theannual interest rate determination model 3920 includes more, less,and/or different outputs than those displayed and/or described above.

The model 3920 relies on several factors when processing inputs 4105 todetermine outputs 4115. Specifically, the model 3920 relies on a baseannual rate (RB) 4125, a geographic adjustment factor (RG) 4130, a loanamount adjustment factor (RA) 4135, a pricing table 4140, a tier 4145,and a maximum loan-to-value adjustment factor (RI) 4150. In someembodiments, the model 3920 relies on more, less, and/or differentfactors to determine outputs 4115 based on inputs 4105.

FIGS. 42 and 43 provides an example of how the model 3920 determinesoutputs 4115 for a car loan. FIGS. 44 and 45 show an example of how themodel 3920 determines outputs 4115 for a vehicle loan for a recreationalvehicle or a boat. In FIGS. 42-45, the model 3920 determines outputs4115 in three steps. Step one relies on several factors to determine anestimated loan amount 4211 to be used in step two. Step two determinesthe estimated annual interest rate 4120. The interest rate 4120 is usedby step two to determine a final loan amount 4221 to be used in stepthree. Step three relies on the final loan amount 4221, along with otherfactors, to determine an annual interest rate 3950. In some embodiments,model may include more, less, and/or different steps. In someembodiments, each step may have a different purpose and/or rely ondifferent factors and/or inputs than those displayed in FIGS. 42-45.

Car Loan Annual Interest Rate Determination Example

FIGS. 42 and 43 display the process and corresponding example used fordetermining the estimated annual interest rate 4120 and annual interestrate 3950 for a vehicle loan for a car. FIG. 42 displays the automobileinterest rate determination environment 4200. Environment 4200 includesstep one 4210, step two 4220, and step three 4230.

In step one 4210, an estimated loan amount 4211 is calculated based onthe present value of a stream of monthly payments for a specific valueat a specific interest rate for a specific term. The specific values ofthe stream of monthly payments can be determined from inputs 4105.Specifically, the specific value of the term may be equal to the policymaximum term 3630, while the monthly payment is calculated based onmethods similar to those described for the payment capacity estimator2410 using inputs 4105. In FIG. 43, the specific value of the monthlypayments is $200 while the specific value of the term is 60 months.

The specific value for the interest rate in step one 4210 equals the sumof the base annual rate 4125, the geographic adjustment factor 4130, andthe maximum loan amount adjustment factor 4213. In FIG. 43, the baserate 4125 equals 5.14%, the geographic adjustment factor 4130 equals0.15%, and the maximum loan amount adjustment factor 4213 equals 2.00%.The maximum loan amount adjustment factor 4213 is determined byselecting the maximum interest rate shown within the appropriate pricingtable 4140, which is 2.00% in FIG. 43. Thus, the specific value of theinterest rate 4212 equals 5.14%+2.00%+0.15%, which is 7.29%. The presentvalue of monthly payments of $200 for 60 months at an annual interestrate of 7.29% equals the estimated loan amount 4211 of $10,031, as seenin FIG. 43.

In step two 4220, a final loan amount 4221 is determined andsubsequently used in step three. The final loan amount 4221 isdetermined by selecting the lowest value between the loan-to-valuecapped loan amount and the debt to income capped loan amount 4110. Thedebt to income capped loan amount 4110 is received as an input 4105 bythe interest rate determination model 3920. The calculation of the debtto income capped loan amount 4110 is described later. The loan-to-valuecapped loan amount is determined by multiplying the policy maximumloan-to-value ratio 3640 and vehicle collateral value, which isdetermined from the vehicle information 1210. In FIG. 43, vehiclecollateral value equals $20,000 while the policy maximum loan-to-valueratio 3640 equals 85%. Thus, the loan-to-value capped loan amount is$17,000.

In order to determine the debt to income capped loan amount 4110, steptwo 4220 must first determine an estimated annual interest rate 4120 tosend to the loan amount calculation engine 3530 for determining amount4110. The rate 4120 equals the sum of the base annual rate 4125, thegeographic adjustment factor 4130, and the two-tiered loan amountadjustment factor 4223. The two-tiered loan amount adjustment factor4223 varies depending on the tier 4145.

In FIG. 43, tier 4145 is determined based on the estimated loan amount4211 in step one 4210. Specifically, the tier 4145 is determined basedon whether the estimated loan 4211 amount is greater than $10,000.Because the estimated loan amount 4211 equals $10,031, the tier 4145 isset to a tier for vehicle loan amounts greater than $10,000. Thetwo-tiered loan amount adjustment factor is determined by selecting themaximum interest rate from a pricing table for the applicable tier,which in this case is for vehicle amounts greater than $10,000. In thedisplayed embodiment of FIG. 43, the two-tiered loan amount adjustmentfactor 4223 equals 0.25%. In other embodiments, if the vehicle loanamount 4211 is less than $10,000, the tier 4145 would be for vehicleamounts below $10,000, which would result in the two-tiered loan amountadjustment factor 4223 equaling 2.00%, which is the maximum interestrate available for the tier for vehicle loan amounts below $10,000.

The estimated annual interest rate 4120 equals the sum of the base rate4125, the geographic adjustment factor 4130, and the two-tiered loanamount adjustment factor 4223. The calculation of this rate 4222(5.14%+0.15%+0.25%) results in an estimated annual interest rate 4120 of5.54%. The estimated annual interest rate 4120 is then used by loanamount calculation engine 3530 to determine the debt to income cappedloan amount 4110. In FIG. 43, the amount 4110 is $10,460. As a result,the final vehicle loan amount 4221 also equals $10,460 because the debtto income capped loan amount 4110 ($10,460) is less than theloan-to-value capped loan amount ($17,000). The amount 4221 is then usedby step three 4230 to calculate the final interest rate 4231, which maybe set as the annual interest rate 3950 outputted by the interest ratedetermination model 3920.

For step three 4230, the final interest rate 4231 equals the sum of thebase rate 4125, the geographic adjustment factor 4130, and the finalloan amount adjustment factor 4232. The final loan amount adjustmentfactor 4232 is determined by using the final loan amount 4221 to selectthe corresponding adjustment factor from the pricing table 4140. In FIG.43, the final vehicle loan amount 4221 of $10,460 corresponds to anadjustment factor of 0.00%. Additionally, the base rate 4125 equals5.14% and the geographic adjustment factor 4130 equals 0.15%. Thus, thefinal interest rate 4231 equals 5.29% (the result of 5.14%+0.15%+0.00%).As a result, the annual interest rate 3950 that is sent as an output4115 by annual interest rate determination model 3920 may be set to thefinal interest rate 4231 of 5.29%. In other embodiments, the annualinterest rate 3950 may be modified from the final interest rate 4231based on pricing and/or policy guidelines. In some embodiments, theoffer generation model 3210 will generate a vehicle loan offer 3550 withan amount of $10,460, a term of 60 months, a maximum monthly payment of$200, and an interest rate of 5.29% for the applicant of FIGS. 42 and43.

Recreational Vehicle Annual Interest Rate Determination Example

FIGS. 44 and 45 display the process and corresponding example used fordetermining the estimated annual interest rate 4120 and annual interestrate 3950 for a vehicle loan for a recreational vehicle or a boat. FIG.44 displays the recreational vehicle rate determination environment4400, which includes step one 4410, step two 4420, and step three 4430.Annual interest rate determination for a recreational vehicle is similarto that of a car. However, a couple of differences exist. First, nogeographic factor 4130 is used when calculating annual interest rate3950 or the estimated annual interest rate 4120. Second, in step two4420 and step three 4430, the loan-to-value adjustment factor 4150 isrelied upon for determining interest rates 4120 and 3950.

In step one 4410, an estimated loan amount 4411 is determined for use bystep two 4420. Estimated loan amount 4411 is calculated based on thepresent value of a stream of monthly payments for a specific value at aspecific interest rate for a specific term. Similar to FIGS. 42 and 43,the specific values of the term and the monthly payment are calculatedbased on inputs 4105. Specifically, the term is determined from thepolicy maximum term 3630 while the maximum monthly payment is calculatedbased on the policy post loan debt to income ratio 3650, and creditbureau data 1220, which includes the applicant's monthly income andpre-loan monthly debt payments. The maximum payment is determined usingmethods similar to those described for the payment capacity estimator2410 using inputs 4105. In FIG. 45, the term equals 120 months while themaximum monthly payment equals $300.

The specific value for the interest rate 4412 equals the sum of the baserate 4125 and the maximum loan amount adjustment factor 4413. In thedisplayed embodiment of FIG. 45, the maximum loan amount adjustmentfactor 4413 is determined by selecting the maximum interest rateavailable in the appropriate pricing table 4140, which in this case is1.00%. The base rate 4125 equals 7.29%. Thus, the specific value of theinterest rate 4412 equals 8.29% (the result of 7.29%+1.00%). In FIG. 45,the estimated loan amount is calculated to be $24,417 based on thepresent value of monthly payments of $300 for 120 months at an annualinterest rate of 8.29%.

Step two 4420 determines the final loan amount to send to step three4430. Final loan amount 4421 is determined by selecting the minimum ofthe loan-to-value capped loan amount and the debt to income capped loanamount 4110. The debt to income capped loan amount 4110 is received asan input 4105 from the loan amount calculation engine 3530. Theloan-to-value capped amount is calculated by multiplying the policymaximum loan-to-value ratio 3640 by the vehicle collateral value, whichis determined from vehicle information 1210. In FIG. 45, the policymaximum loan-to-value ratio 3640 equals 90% while the vehicle loanamount collateral value equals $27,000. Thus, the loan-to-value cappedloan amount equals $24,300.

To determine the debt to income capped loan amount 4110, an estimatedannual interest rate 4120 must first be determined at step 4420. Therate 4120 is then used by the loan amount calculation engine 3530 todetermine the loan amount 4110. The rate 4120 equals the sum of the baserate 4125, the maximum loan-to-value adjustment factor 4424, and thetwo-tiered loan amount adjustment factor 4423. The two-tiered loanamount adjustment factor 4423 varies depending on the tier 4145. Thetier 4145 is set based on whether or not the estimated loan amount value4411 is greater than or less than $10,000.

In FIG. 45, tier 4145 is set to the tier representing vehicle loanamounts greater than $10,000 because the estimated loan amount 4411 of$24,417 is greater than $10,000. Thus, the two-tiered loan amountadjustment factor 4423 equals the maximum rate available in pricingtable 4140 for the tier 4145 representing vehicle loan amounts that aregreater than $10,000. In this case, the corresponding adjustment factor4423 equals 1.00%. In other embodiments, if the vehicle loan amount 4411is less than $10,000, the tier 4145 would be set at a tier representingvehicle loan amounts below $10,000. In this case, the two-tiered loanamount adjustment factor 4423 would equal 0.15%, which is the maximuminterest rate available in pricing table 4140 for vehicle loan amountsbelow $10,000.

For step two 4420, the maximum loan-to-value adjustment factor 4424 iscalculated by selecting the maximum interest rate available in thepricing table 4140. Thus, in FIG. 45, the maximum loan-to-valueadjustment factor 4424 equals 0.75%. As a result, the estimated annualinterest rate 4120, which equals the sum of the base rate 4125, thetwo-tiered loan amount adjustment factor 4423, and the maximumloan-to-value adjustment factor 4424 (7.29%+1.00%+0.75%), is 9.04%.

The estimated annual interest rate 4120 of 9.04% is then sent as anoutput 4115 to the loan amount calculation engine 3530. The loan amountcalculation engine 3530 uses the interest rate 4120 to determine thedebt to income capped loan amount 4110. In FIG. 45, the debt to incomecapped amount 4110 is calculated to be $23,642. Consequently, step two4420 determines the final amount 4421 to equal the amount 4110 of$23,642 because the amount 4110 is less than the loan-to-value cappedamount of $24,300.

Step three 4430 then calculates a final interest rate 4431 by using thefinal amount 4421 to determine a final loan amount adjustment factor4432 and a final loan-to-value adjustment factor 4433. The finalinterest rate 4431 is calculated based on the sum of the base rate 4125,the final loan-to-value adjustment factor 4433 and the final loan amountadjustment factor 4432. Final interest rate 4431 may then be outputtedas the annual interest rate 3950 of the annual interest ratedetermination model 3920. Alternatively, the rate 4431 may be modifiedand then set as interest rate 3950 to ensure that interest rate complieswith policy and/or pricing guidelines.

In FIG. 45, the final loan amount adjustment factor 4432 is determinedby selecting from pricing table 4140 the interest rate corresponding tothe final loan amount 4421 of $23,642. In this case, the correspondingfinal loan amount adjustment factor 4432 equals 0.00%. The finalloan-to-value adjustment factor 4433 is also determined by selectingfrom pricing table 4140 the interest rate corresponding to the maximumloan-to-value ratio, which in this case is 0.9. Thus, the finalloan-to-value adjustment factor 4433 equals −0.25%. As a result, thefinal interest rate 4431 equals the sum of the base rate 4125, the finalloan amount adjustment factor 4432, and the final loan-to-valueadjustment factor 4433 (7.29%+0.00%−0.25%), which is 7.04%. Thus, theannual interest rate determination model 3920 may output an annualinterest rate 3950 of 7.04%. In some embodiments, the offer generationmodel 3210 will generate a vehicle loan offer 3550 with an amount of$23,642, a term of 120 months, a maximum monthly payment of $300, and aninterest rate of 7.04% for the applicant of FIGS. 44 and 45.

In the displayed embodiments of FIGS. 42-45, the processes and examplesmay include more, less, and/or different steps, purposes, factors,inputs, outputs, and/or other components than those displayed in thefigures and/or described above.

Offer Generation Model: Loan Amount Calculation Engine

FIG. 46 displays a block diagram of the loan amount calculationenvironment 4600. Environment 4600 includes the loan amount calculationengine 3530, inputs 4605, and outputs 4615. The purpose of engine 3530is to determine the appropriate vehicle loan amount based on thereceived inputs and other factors. The inputs 4605 include pricingmaximum term 3940, policy maximum loan-to-value ratio 3640, policy postloan debt to income ratio 3650, estimated annual interest rate 4120,vehicle information 1210, credit bureau data 1220, and custom inputs3206. The estimated annual interest rate 4120 is received from theannual interest rate determination model 3920, which is explained abovein FIG. 41. Vehicle information 1210 may include the vehicle collateralvalue. Credit bureau data 1220 may include the monthly income and themonthly pre-loan debt payments of the applicant. Custom inputs 3206 maybe provided by the offer customization model 3220, which is describedlater. In some embodiments, the inputs 4605 include more, less, and/ordifferent inputs than those displayed and/or described above.

Similar to the annual interest rate determination model 3920 displayedin FIG. 41, received inputs 4605 may be used to calculate other inputsand factors. For example, credit bureau data 1220 and policy post loandebt to income ratio 3650 may be used to calculate a maximum monthlyloan payment for an applicant using methods similar to those describedfor payment capacity estimator 2410. Specifically, the maximum paymentfor an applicant may be calculated by first multiplying the policy postloan debt to income ratio 3650 by the applicant's monthly income, andthen subtracting that product by the applicant's pre-loan monthly debtpayments to determine the maximum loan payment the applicant can afford.In other embodiments, other factors and/or inputs may be calculatedbased on inputs 4605.

The engine 3930 processes inputs 4605 to determine outputs 4615, whichinclude the vehicle loan amount 4620. The vehicle loan amount mayrepresent the principal leant and owed by the applicant for the vehicleloan. Alternatively, the vehicle loan amount may represent the principaland interest payments owed by the applicant during the loan. The vehicleloan amount 4620 corresponds to the vehicle loan amount for a vehicleloan offer to an applicant. Thus, the amount 4620 is used by the offergeneration model 3210 to generate the vehicle loan offer 3550.Additionally, the amount 4620 may be provided with offer 3550. In someembodiments, the loan amount calculation engine 3530 includes more,less, and/or different outputs than those displayed and/or describedabove.

The vehicle loan amount 4620 is determined by engine 3530 by selectingthe minimum amount of two other calculated amounts. Specifically, theengine 3530 calculates a loan to value capped amount 4610 and a debt toincome capped amount of 4110. The loan to value capped amount 4610 isdetermined by multiplying the policy maximum loan-to-value ratio 3640 bythe vehicle collateral value (which is received as part of vehicleinformation 1210).

The debt to income capped amount 4110 equals the present value of astream of monthly payments of a specified amount occurring for aspecified term at a specified interest rate. The specified amount equalsthe maximum monthly loan payment, which can be calculated based onsimilar methods used for the payment capacity estimator 2410. Specifiedterm equals the pricing maximum term 3940. Specified monthly interestrate equals the estimated annual interest rate 4120 divided by 12.

Once the engine 3530 determines the loan-to-value capped amount 4610 andthe debt to income capped amount 4110, the engine selects the lesser ofthe two amounts (4110 or 4610) to output as amount 4620. In someembodiments, the engine 3530 may use more, less, and/or differentmethods to determine an amount 4620 than those displayed in FIG. 46and/or described above.

The loan amount calculation engine 3530 may be called by the offergeneration model 3210 to generate an amount for offer 3550. In someembodiments, the offer generation model 3210 may call the engine 3530for three different vehicle loans. The three different vehicle loans mayonly differ based on the term. Specifically, the three vehicle loansinclude a first vehicle loan at a maximum term, a second vehicle loanwith a term that is 12 months shorter than the maximum term, and a thirdvehicle loan with a term that is 24 months shorter than the maximumterm. All other information for the vehicle loans, such as theapplicant, applicant information, vehicle, vehicle information, etc. maybe the same. The interest rate provided for the three vehicle loanoffers may or may not be the same for the three vehicle loans. Thevehicle loan amounts for each of the three vehicle loans may or may notdiffer. In some embodiments, the first, second, and third vehicle loansdescribed above correspond to the first offer 3230, second offer 3235,and third offer 3240 generated by multiple offers model 250. In otherembodiments, the engine 3530 is called for more than or less than threevehicle loans.

Also, the loan amount calculation engine 3530 may be called by the offercustomization model 3220 to determine the amount for a customizedvehicle loan offer. In this embodiment, the model 3220 may providecustom inputs 3206 to the engine 3530. The custom inputs 3206 may alterthe term or the amount of the vehicle loan. The loan amount 4620generated by engine 3530 may be part of a customized offer 3550outputted by the offer generation model 3210. Further, the loan amount4620 may be part of a recalculated offer 3245 generated by the multipleoffers model 250 in response to a user inputting custom inputs 3206. Inother embodiments, the engine 3530 may be called by other models fordifferent purposes than those described above.

Offer Generation Model: Screenshot

FIG. 47 displays a screenshot of three personalized vehicle loan offersgenerated for an applicant. The screenshot 4700 includes vehicle loancalculation information 4710, a first vehicle loan offer 4720, a secondvehicle loan offer 4730, and a third vehicle loan offer 4740. Thescreenshot also includes start over button 4750, loan calculation button4760, and offer customization button 4770. In some embodiments, thefirst, second, and third vehicle loan offers 4720, 4730, and 4740correspond to the first offer 3230, second offer 3235, and third offer3240, respectively, generated by multiple offers model 250. The threepersonalized offers may be generated in response to one received vehicleloan application for an applicant. In some embodiments, the screenshot4700 includes more, less, and/or different items than those displayed inFIG. 47.

In the displayed screenshot, the start over button 4750 allows the userto restart the multiple offer generation process. In some embodiments,the user re-enters inputs used for generating multiple offers. Theinputs may include applicant information and/or vehicle information. Theloan calculation button 4760 allows the user to recalculate multiplevehicle loan offers for an applicant. In some embodiments, the user maybe able to change some of the data provided as inputs for calculatingthe vehicle loan offers. In some embodiments, the system displays theinputs provided by the user in response to a selection of the loancalculation button 4760. The user can then modify the inputs andrecalculate the multiple vehicle loan offers. The offer customizationbutton 4770 allows the user to generate customized offers. In someembodiments, the user can customize an offer based on the vehicle loanterm and/or the vehicle loan amount. Additional details regarding offercustomization are described later with respect to offer customizationmodel 3220. In some embodiments, buttons 4750, 4760, and 4770 can domore, less, and/or different functions than those described above.

Each of the three offers 4720, 4730, and 4740 displays an estimatedmonthly payment, an estimated annual interest rate (APR), a loan term inmonths, and a loan amount. The vehicle loan calculation information 4710explains that the maximum vehicle loan amount, shown in offer one 4720,is calculated based on a specified collateral valuation. In otherembodiments, the information 4710 may display more, less, and/ordifferent information than the information shown in FIG. 47. In someembodiments, each offer may display more, less, and/or differentinformation than the information shown in FIG. 47.

In the displayed embodiment, the monthly payment is approximately thesame payment for all three offers (approximately $300). However, eachoffer has a different term. Specifically, the first offer 4720 is amaximum term of 84 months, while the second offer 4730 has a term thatis 12 months shorter (72 months) and the third offer has a term that is24 months shorter (60 months). In some embodiments, the displayed offersmay be incremented by more or less months than the 12 month incrementsshown in FIG. 47.

As the term is reduced for each vehicle loan offer (offers 4730 and4740), the loan amounts are also reduced. Additionally, in the displayedembodiment, the vehicle loan interest rates are also reduced as the termis reduced. While the displayed embodiment shows the monthly paymentbeing fixed, the term being decremented, and the loan amount andinterest rate being determined accordingly, in other embodiments, anyone of the monthly payment, interest rate, vehicle loan term, and/orvehicle loan amount can be fixed, decremented, incremented, oraccordingly calculated when determining three personalized offers for anapplicant.

For example, in one embodiment, the vehicle loan amount is fixed whilethe term is decremented. In this embodiment, the interest rate and themonthly payment would be calculated accordingly for the three differentoffers. In another embodiment, the vehicle loan amount and vehicle loanterm may be fixed, while the interest rate is decremented. In thisembodiment, the monthly payment would be calculated, accordingly. Thus,the parameters of the personalized vehicle loan offers can be fixed,decremented, incremented, and/or calculated as needed by an organizationto meet the needs of a vehicle loan applicant.

Offer Customization Model

FIG. 48 displays a block diagram of the offer customization environment4800. Environment 4800 includes the offer customization model 3220,inputs 4805, and outputs 4815. The purpose of the model 3220 is to allowa user to further customize vehicle loan offers for an applicant. Insome embodiments, the user may want to further customize vehicle loanoffers for an applicant after receiving multiple vehicle loan offersbased on inputs provided by the user. For example, the user may not besatisfied with the vehicle loan conditions specified by the multipleoffers, and thus, requires a customized vehicle loan offer withdifferent vehicle loan conditions.

The model 3220 receives inputs 4805, which include custom inputs 3206.The custom inputs may include user designations for a vehicle loanamount and/or vehicle loan term. In other embodiments, other custominputs are received by model 3220. The model 3220 processes the inputs4805 to generate outputs 4815, which include a custom loan amount 4820and a custom loan term 4825. The outputs 4815 may be provided to offergeneration model 3210, policy guidelines 3510, pricing model 3520,and/or loan amount calculation engine 3530. Alternatively, the outputs4815 may be provided to other models or engines that require the outputsfor customizing the vehicle loan. The custom inputs may cause a vehicleloan amount, term, interest rate, and/or monthly payment to be changed.The custom inputs may result in a recalculated offer 3245 beinggenerated by the multiple offers model 250.

The model 3220 may process inputs 4805 by modifying the inputs to complywith various policies and constraints. For example, if a user inputs aterm above the maximum term allowed, the model 3220 may modify thecustom inputs to equal the allowed maximum term. In other embodiments,the model 3220 may reject the noncompliant custom vehicle loan term asan input and require the user to input a compliant custom vehicle loanterm. By processing inputs 3206, model 3220 can ensure generatingcompliant outputs 4815, including a compliant custom loan amount 4820and a custom loan term 4825. In other embodiments, the model 3220includes more, less, and/or different inputs, outputs, functions, and/orcomponents than those displayed in FIG. 48 and/or described above.

FIG. 49 displays a screenshot 4900 of the system generating andproviding a customized offer to a user. The screenshot 4900 includesvehicle loan offers block 4705, customized offer block 4905, vehicleloan term customization block 4920, vehicle loan amount customizationblock 4940, and back button 4960. In some embodiments, the screenshot4900 may include more, less, and/or different items than those shown.

Similar to FIG. 47, vehicle loan offer block 4705 includes a firstvehicle loan 4720, a second vehicle loan 4730, and a third vehicle loan4740. Customized offer block 4905 includes customized vehicle loan offer4910. Similar to FIG. 47, in some embodiments, the first, second, andthird vehicle loan offers 4720, 4730, and 4740 correspond to the firstoffer 3230, second offer 3235, and third offer 3240, respectively,generated by multiple offers model 250. Further, the customized vehicleloan offer 4910 may correspond to the recalculated vehicle loan offer3245 generated by multiple offers model 250, in some embodiments.

For each vehicle loan offer displayed (offers 4720, 4730, 4740, and4910), the displayed offer includes an estimated monthly payment,estimated annual interest rate (APR) a loan term displayed in months,and a loan amount displayed in dollars. The displayed offer alsoincludes a select and continue button (button 4706 for offer 4720,button 4707 for offer 4730, button 4708 for offer 4740, and button 4906for offer 4910). A user can select any one of these buttons to selectthe corresponding vehicle loan offer displayed. For example, a user canselect button 4906 to choose the customized vehicle loan offer 4910.Alternatively, user can select button 4708 to select the correspondingvehicle loan offer 4740. Although not displayed, select and continuebuttons 4706, 4707, and 4708 may also be displayed for theircorresponding offers (4720, 4730, and 4740, respectively) in screenshot4700 of FIG. 4710. In some embodiments, each displayed offer may includemore, less, and/or different parameters than those displayed in FIG. 49.In some embodiments, each offer may include more, less, and/or differentbuttons to provide more, less, and/or different functions than thosedisplayed in FIG. 49 and/or described above. In some embodiments, thescreenshot 4900 may display more, less, and/or different offers thanthose shown in FIG. 49.

Screenshot 4900 also includes back button 4960. A user can select backbutton 4960 to navigate to the previous screen. In some embodiments, theprevious screen displays the offers shown in the vehicle loan offerblock 4705. In some embodiments, selection of the back button 4960 by auser displays the screenshot 4700 displayed in FIG. 47. However in otherembodiments, a different screen may be generated in response to aselection of the back button 4960 by a user.

Vehicle loan term customization block 4920 allows a user to customize avehicle loan by modifying the vehicle loan term. The block 4920 includesa vehicle loan term selection 4921, a vehicle loan term minimum 4925, avehicle loan term maximum 4926, a vehicle loan term range box 4930, anda vehicle loan term slider 4931. The loan term minimum 4925 and loanterm maximum 4926 correspond to the minimum and maximum vehicle loanterms available in the vehicle loan term box 4930. The vehicle loan termbox 4930 may have shaded and unshaded portions. The shaded portion ofbox 4930 corresponds to the range of compliant vehicle loan terms. Theunshaded portion of box 4930 represents the range of noncompliantvehicle loan terms. Slider 4931 allows a user to select a vehicle loanterm available in box 4930. In some embodiments, the user is onlypermitted to move the slider 4931 within the shaded portion of the box(compliant vehicle loan terms). In other embodiments, the user can movethe slider 4931 to any vehicle loan term available in box 4930. If theuser moves the slider 4931 to a noncompliant vehicle loan term, thesystem provides an indication to the user that the selected term isnoncompliant. For example, the system may display a message stating thatthe selected vehicle loan term is noncompliant. When the user selects avehicle loan term with the slider 4931, the vehicle loan term selection4921 is populated with the selected vehicle loan term. In otherembodiments, box 4920 includes more, less, and/or different componentsthan those displayed in FIG. 49.

Vehicle loan amount customization block 4940 allows a user to customizea vehicle loan by modifying the vehicle loan amount. The block 4940includes a vehicle loan amount selection 4941, a vehicle loan amountminimum 4945, a vehicle loan amount maximum 4946, a vehicle loan amountbox 4950, and a vehicle loan amount box slider 4951. The operation ofthe block 4940 and its components are similar to block 4920 and itscomponents. Thus, the box 4950 has a maximum selectable amount 4946, aminimum selectable amount 4945, a shaded portion representing compliantvehicle loan amounts, and an unshaded portion representing noncompliantvehicle loan amounts. The user may select a vehicle loan amountdisplayed within the box 4950. However, compliant vehicle loan amountselection can only be selected within the shaded portion of box 4950.Once the user makes his selection via the slider 4951, the selectedvehicle loan amount is displayed in vehicle loan amount selection 4941.In some embodiments, the block 4940 includes more, less, and/ordifferent components than those described above and displayed in FIG.49.

FIG. 50 includes a screenshot 5000 of a selected vehicle loan offer. Thescreenshot 5000 includes vehicle loan details 5010 and vehicle loancalculation information 5020. The vehicle loan details 5010 correspondto the selected vehicle loan offer. In the displayed embodiment, thedetails 5010 include the monthly payment, the annual interest rate(APR), the vehicle loan term displayed in months, the vehicle loanamount displayed in dollars, and a collateral value displayed indollars. In other embodiments, details 5010 may include more, less,and/or different items than those displayed. Vehicle loan calculationinformation 5020 includes information used for calculating the selectedvehicle loan. In the displayed embodiment, information 5020 providesinformation regarding the applicant income and the collateral valuation.However, in other embodiments, the information 5020 may include more,less, and/or different information regarding the calculation of theselected vehicle loan. In some embodiments, screenshot 5000 may includemore, less, and/or different items than those displayed in FIG. 50and/or described above.

Skill Based Routing Model

The skill based routing model 260 is called upon when the automatedunderwriting model 220 determines that a vehicle loan applicationrequires manual underwriting. The skill based routing model attempts toimprove the assignment of referred vehicle loan applications to creditanalysts. Skill based routing model 260 uses a loan complexity model anda loan allocation engine to accomplish this goal. The loan complexitymodel categorizes loans into complexity groups based on the expectedloan processing time. Meanwhile, the loan allocation engine assignsapplications to credit analysts based on the authority of the analyst,the availability of the analyst, fair allocation limits, and thedetermined loan complexity. By improving the assignment of referredapplications, the skill based routing model 260 empowers the vehicleloan generation system 200 to turn around vehicle loan applications thatrequire manual underwriting faster. In some embodiments, the creditanalysts are part of the organization, 101. In some embodiments, thecredit analysts are part of a different organization, such asunderwriting organization 170.

FIG. 51 displays a block diagram of the skill based routing modelenvironment 5100. The environment 5100 includes inputs 5105, skill basedrouting model 260, and outputs 5115. The inputs 5105 include vehicleinformation 1210, credit bureau data 1220, and loan information 1230.Vehicle information 1210 may include a product type, a vehiclecollateral amount, and/or other information about the vehicle. Creditbureau data 1220 may include the pre-loan monthly debt payment for anapplicant, a FICO score for an applicant, monthly income for anapplicant, a pre-loan debt to income ratio for an applicant, and/orother information about the applicant. In some embodiments, the creditbureau data may be used to calculate other inputs and factors. Forexample, the applicant's pre-loan debt to income ratio may be calculatedby dividing the applicant's pre-loan monthly debt payments by theapplicant's monthly income. Loan information 1230 may include anapplication type and/or other information about the loan. In someembodiments, inputs 5105 include more, less, and/or different inputsthan those displayed in FIG. 51.

The inputs 5105 are processed by model 260 to generate outputs 5115,which include analyst 5130 and decision 5140. Analyst 5130 designatesthe credit analysts that will process the loan application to determinea decision for underwriting. Decision 5140 is generated by the analyst5130, in some embodiments. The decision 5140 may be to approve or denythe vehicle loan application for underwriting. In some embodiments, thedecision 5140 is to refer the application for further processing. Insome embodiments, outputs 5115 include more, less, and/or differentoutputs than those displayed in FIG. 51.

When model 260 processes inputs 5105 to generate outputs 5115, model 260relies on loan complexity model 5110 and a loan allocation engine 5120.The loan complexity model 5110 and the loan allocation engine 5120 arediscussed in further detail in later figures. In some embodiments, themodel 260 includes more, less, and/or different models, engines, and/orcomponents than those displayed in FIG. 51.

Loan Complexity Model

FIG. 52 displays a block diagram of the loan complexity modelenvironment 5200. Environment 5200 includes a loan complexity model 5110and outputs 5215. The purpose of model 5110 is to analyze, determine,and categorize the complexity of a vehicle loan. By knowing whether ornot a vehicle loan is simple, complex, or regular, vehicle loans can bemore efficiently assigned to analysts with an appropriate skill levelfor processing the loan. By efficiently assigning vehicle loans based oncomplexity, loan processing time is reduced, which allows theorganization to generate more vehicle loans and keep customers satisfiedby turning loans around faster.

Model 5110 generates outputs 5215, which includes loan complexity 5270.In some embodiments, the loan complexity 5270 may categorize a loan assimple, regular, or complex. However, more, less, and/or differentcategorizations of loan complexity may exist. In some embodiments, model5110 includes more, less, and/or different outputs 5215 than thosedisplayed in FIG. 52.

To generate outputs 5215, model 5110 relies on several factors forprocessing. For a vehicle loan, model 5110 determines loan complexitybased on the applicant's pre-loan monthly debt payment 5210, the vehicleloan product type 5220, vehicle collateral amount 5230, the applicant'spre-loan debt to income ratio 5240, the applicant's FICO score 5250, andthe vehicle loan application type 5260. In some embodiments, the model5110 relies on more, less, and/or different factors than those displayedin FIG. 52. The model 5110 uses the displayed factors to generateoutputs 5215.

In some embodiments, loan complexity is based on loan processing time.Specifically, for loans requiring a low processing time, model 5110 maycategorize the complexity of these loans as “simple.” Alternatively, forloans requiring large processing time, on a 5110 may categorize thecomplexity of these loans as “complex.” Further, for loans requiring amedium amount of loan processing time, model 5110 may categorize thecomplexity of these loans is “regular”. In some embodiments, model 5110may categorize the complexity of vehicle loans based on more, less,and/or different factors than those described above and displayed inFIG. 52. For example, the monetary cost, resources used, manpower used,previous loan processing experience, and/or other factors may beconsidered while categorizing the complexity of the loan.

FIG. 53 displays loan processing time environment 5300. Environment 5300includes processing time block 5310. Only the steps displayed withinblock 5310 count towards loan processing time. Thus, loan processingtime is equal to the sum of time it takes for an analyst to work on aloan from the time the analyst starts working on the loan until the timethe analyst makes a final decision. Thus, tasks such as data entry arenot considered part of loan processing time. In the displayedembodiment, an underwriting decision made by the analyst may includeapproving the loan for underwriting, declining the loan,counteroffering, and/or conditionally approving the loan forunderwriting. In some embodiments, block 5310 includes more, less,and/or different items as processing time than those displayed in FIG.53 and/or discussed above.

FIG. 54 displays a block diagram of the loan complexity segmentationenvironment 5400. Environment 5400 displays the decision tree used bymodel 5110 to determine the loan complexity based on several factors.The decision tree includes decisions based on pre-loan monthly debtpayments 5210, product type 5220, vehicle collateral amount 5230, thepre-loan debt to income ratio 5240, FICO score 5250, and the applicationtype 5260. In some embodiments, more, less, and/or different factors areincluded in the decision tree for determining loan complexity. Also,each decision within the decision tree may include more, less, and/ordifferent decisions than those displayed in FIG. 54.

The decision tree displays several different vehicle loan complexitysegments. The loan complexity decision tree includes “simple” loancomplexity segments 5410, “regular” loan complexity segments 5420, and“complex” loan complexity segments 5430. Generally, vehicle loansfalling in the simple vehicle loan complexity segment 5410 have anaverage loan processing time below six minutes. Vehicle loans falling inthe complex vehicle loan complexity segment 5430 generally have anaverage loan processing time greater than 10 minutes. Vehicle loans withan average loan processing time between 6 minutes and 10 minutes arecategorized as part of the “regular” vehicle loan complexity segment5420. In some embodiments, more, less, and/or different vehicle loansegments may be used for categorizing vehicle loan complexity. In someembodiments, the ranges of average loan processing time may be larger,smaller, and/or different ranges for vehicle loan complexity segmentsthan those described above for FIG. 54.

The purpose of environment 5400 is to help model 5110 determine vehicleloan complexity for a vehicle loan based on several factors. Thedisplayed decision tree in the environment was generated by collectingand analyzing various vehicle loans and their corresponding processingtimes over a nine month period. Vehicle loan complexity segments werethen generated based on the average processing times recorded for thevehicle loans. As a result, the displayed decision tree enables model5110 to determine vehicle loan complexity for a vehicle loan based onseveral factors associated with the vehicle loan.

For example, a $50,000 vehicle loan for a car to an applicant, John Doe,with pre-loan monthly debt payments 5210 of $5000 and a pre-loan monthlyincome of $15,000 (resulting in a pre-loan debt to income ratio of 33%)would be considered a complex vehicle loan that falls in segment 5430.Alternatively, if John Doe had pre-loan monthly debt payments of $2500,a monthly income of $10,000 (resulting in a pre-loan debt to incomeratio of 25%), a FICO score of 830, and a joint application type, thenJohn Doe's $50,000 vehicle loan for a car would be categorized as asimple vehicle loan falling under segment 5410. In another alternative,if John Doe had pre-loan monthly debt payments of $2500, pre-loanmonthly income of $5000 (resulting in a pre-loan debt to income ratio of50%), and a FICO score of 750, John Doe's vehicle loan application wouldlikely be considered a “regular” complexity application falling undercomplexity segment 5420. Thus, the decision tree of environment 5400enables model 5110 to analyze various factors of a vehicle loan todetermine the vehicle loan complexity.

Loan Allocation Engine

FIG. 55 displays a block diagram of loan allocation engine environment5500. The purpose of the loan allocation engine is to efficientlyallocate loans for processing to various analysts based on a number offactors. By doing so, engine 5120 can prevent a group of analysts frombeing overused, underused, or being asked to process loans that are toodifficult, or too simple. For example, in some embodiments, thedistribution of complexity of the loans may not match the distributionof skill level of the analysts. Specifically, an organization may havethe majority of available applications be categorized with a complexitylevel of “simple,” but have a pool of analysts with the majority of theanalysts being capable of handling complex loans. By considering variousfactors, the engine 5120 can ensure that “simple” loans are processed byanalysts with an appropriate authority level (i.e., low authority level,analyst authority of $35,000) where possible.

Environment 5500 includes inputs 5505, loan allocation engine 5120, andoutputs 5515. In FIG. 55, inputs 5505 include loan complexity 5270.However, in other embodiments, inputs 5505 include more, less, and/ordifferent inputs than those displayed.

Engine 5120 processes inputs 5505, along with other factors, to generateoutputs 5515, which include analyst 5550. In some embodiments, theoutputted chosen analyst 5550 may be set as the analyst 5130 outputtedby the skill based routing model 260. In other embodiments, theoutputted analyst 5550 may be processed further before being sent as theanalyst 5130 outputted by skill based routing model 260. In someembodiments, the engine 5120 outputs more, less, and/or differentoutputs 5515 than those displayed in FIG. 55.

Engine 5120 relies on various inputs and factors to generate outputs5515, including a chosen analyst 5550 for processing the vehicle loan.In the displayed embodiment of FIG. 55, engine 5120 chooses an analyst5550 from a pool of possible analysts for processing the vehicle loanbased on several factors. These factors include analyst eligibility5510, analyst availability 5520, vehicle loan complexity 5530 (which isdetermined from the vehicle loan complexity 5270 input), and fairallocation limits 5540. In some embodiments, engine 5120 relies on more,less, and/or different factors than those displayed in FIG. 55.

FIG. 56 displays the analyst eligibility table 5600. The loan allocationengine 5120 relies on table 5610 to determine analyst eligibility 5510.Table 5600 includes vehicle loan amount 5610 and analyst lendingauthority 5620. In the displayed embodiment, analysts may be authorizedto process vehicle loans up to $35,000, $50,000, or $75,000. In otherembodiments, other lending authority limits exist that are differentfrom the limits displayed. In some embodiments, analysts may have a lowauthority, a medium authority, or a high authority. In this embodiment,higher authority analysts can handle vehicle loans that are more complexand/or for larger amounts than lower authority analysts. Vehicle loanamounts falling within a designated lending authority result in thecorresponding analyst being categorized as eligible. Alternatively,vehicle loan amounts that exceed an analyst's lending authority causethe analyst to be categorized as “not eligible” for processing thevehicle loan. Thus, engine 5120 considers analysts to be eligible forprocessing vehicle loans with loan amounts that are within the analyst'slending authority. Table 5600 displays one embodiment of this concept.In other embodiments, analyst eligibility may be determined based onmore, less, and/or different factors than vehicle loan amounts andanalyst lending authority.

Engine 5120 also relies on analyst availability 5520 when determiningwhich analysts 5550 to select for processing a vehicle loan. Engine 5120may exclude and/or disadvantage unavailable analysts from being chosenas analyst 5550 to process the vehicle loan. By excluding and/ordisadvantaging unavailable analysts, engine 5120 ensures that vehicleloan processing will be more efficient, since the selected analyst 5550will be, or is more likely to be, available to process the vehicle loan.Analysts may be considered unavailable if they are on vacation, sick,unavailable for processing the loan, out of the office, or for any otherreason that may prevent them from processing the vehicle loan in atimely fashion. Otherwise, the analyst may be considered available.

FIG. 57 displays loan complexity table 5700. Table 5700 provides loancomplexity limits for analysts based on several factors, including loancomplexity 5720 and analyst authority level 5710. In some embodiments,more, less, and/or different factors may be used for determining loancomplexity limits for an analyst. The limits displayed in table 5700 areexpressed as percentages. The percentages represent the percent ofvehicle loans that can have a selected loan complexity relative to thetotal number of loans being processed by a particular analyst. Thus, inthe displayed embodiment, an analyst with an authority level of $35,000is limited to having 50% of his loans be simple, 30% of his loans beregular, and 20% of his loans be complex. Alternatively, an analyst witha higher authority level, such as $75,000 can have as many as 50% of hisloans be complex, 30% of his loans be regular, and 20% of his loans besimple. By setting loan complexity limits, the engine 5120 ensures thatloans are still routed to lower authority level analysts, even whenhigher authority level analysts are available.

FIG. 58 displays a vehicle loan analyst prioritization table 5800. Thetable 5800 displays a prioritization scheme for vehicle loans based onseveral factors, including vehicle loan amount 5810, analyst authoritylevel 5820, and vehicle loan complexity 5830. The prioritization schemedisplayed in table 5800 ensures that the loan complexity of an assignedloan matches the analyst authority level of an assigned analyst. Forexample, the first priority of an analyst with the highest authoritylevel is to process any loan that is categorized as complex, regardlessof the vehicle loan amount. Alternatively, the first priority of thelowest analyst authority level ($35,000) is to handle simple loans thatare less than $35,000. In some embodiments, more, less, and/or differentfactors than those displayed in table 5800 could be used to generate avehicle loan prioritization scheme.

FIG. 59 displays an analyst fair allocation limits environment 5900. Theenvironment 5940 allows the loan allocation engine 5120 to consider thefair allocation limits factor 5540 when determining which loans toassign to a particular analyst. In the displayed embodiment, theenvironment 5900 displays fair allocation limits 5930 for exemplarynumbers of applications (e.g., 30 applications, 300 applications, or 900applications) based on analyst authority 5910. Environment 5900determines these limits 5930 based on several calculations 5920.

The purpose of fair allocation limits 5540 is to ensure that loanallocation is consistent with the number of analysts available and theskill of the available analysts. One possible benefit of using fairallocation limits is preventing a group of analysts from beingoverburdened or underutilized with too many or too few vehicle loans toprocess based on the ability of that group of analysts.

In the displayed embodiment, calculations 5920 consider the averageprocessing time 5921 for each analyst authority 5910, a weighted averageprocessing time 5922, the number of analysts available 5923 for eachanalyst authority, and a weighted number of analysts available 5924 todetermine a weighted analyst distribution 5925 which guides the fairallocation limits 5930. Weighted processing time and/or analystavailability is used because higher authority analysts typically processloans faster than lower authority analysts. As result, an analyst with$75,000 authority can complete more work than an analyst with a lowerauthority during a fixed amount of time. Thus, lower authority analystsshould be assigned fewer and/or simpler loan applications for processingthan higher authority analysts. This goal is met by using a weightedaverage processing time and a weighted number of analysts to calculateanalyst distribution and fair allocation limits.

In the displayed embodiment, calculations 5920 reveal that the averageprocessing times 5921 for a $35,000 analyst is 10 minutes, the averageprocessing time for a $50,000 analyst is 3.75 minutes, and the averageprocessing time for a $75,000 analyst is 2.5 minutes. Thus, the weightedaverage time 5922 shows that a $35,000 analyst takes four times as long(10 minutes divided by 2.5 minutes) and a $50,000 analyst takes 1.5times (3.75 minutes divided by 2.5 minutes) as long as a $75,000analyst. Given these weighted average times 5922, the number of analystsavailable in each pool 5923 is scaled based on these weighted averagetimes 5922 to calculate a weighted number of analysts available 5924.The weighted number of analysts available 5924 is then used to calculatean appropriate weighted analyst distribution 5925 that is used togenerate fair allocation limits 5930. Therefore, even though eightanalysts are available at the $35,000 authority level (number ofanalysts 5923), they are scaled down by a factor of four (weightedaverage time 5922) to two analysts available (weighted number ofanalysts 5924). Similarly, the twelve $50,000 analysts available (numberof analysts 5923) are scaled down to eight analysts available (weightednumber of analysts 5924). Lastly, the twenty $75,000 analysts (5923)remain as twenty available analysts (5924) because no weighting isrequired. The resulting weighted analyst distribution 5925 that isdisplayed (6.7% for $35,000 analysts, 26.7% for $50,000 analysts, and66.6% for $75,000 analysts) then dictates the appropriate fairallocation limits 5930 based on the number of applications that need tobe processed.

Additionally, some of the values displayed in FIG. 59 can be updated asthe data available is updated. For example, fair allocation limits 5930can be updated in real time any time the organization receives a newloan application. Alternatively, average processing time can be updatedquarterly, or more or less frequently, to determine a more accurateweighting system, and thus more effective allocation limits.Furthermore, the number of analysts available in each pool can beupdated daily, or more or less frequently, to also better calculate theweighted analyst distribution, and thus the fair allocation limits 5930.

FIG. 60 displays a vehicle loan analyst tier environment 6000.Environment 6000 is another embodiment in which the vehicle loans can beefficiently allocated to the appropriate analysts. Higher tiers indicateless preferable analysts. Environment 6000 shows the tiers 6050 arebased on analyst eligibility 6010, fair allocation limits 6020, vehicleloan complexity 6030, and analyst availability 6040. Analysts falling intier 1 are considered the first preference for the vehicle loan to beassigned. Vehicle loans will only be assigned to a higher tiered analystwhen the lower tiered analyst is unavailable.

For example, a vehicle loan of $20,000 categorized as “simple”complexity may be allocated to an analyst. At step 6010, a vehicle loancan only be allocated to an analyst within tiers one through eight ifthe analyst is eligible. If the analysts are ineligible, the vehicleloan cannot be allocated to any of the analysts within tiers 1 through8. At step 6020, analysts for whom the vehicle loan will exceed the fairallocation limit are designated within tiers 5 through tier 8.Alternatively, analysts for whom the vehicle loan will fit within thefair allocation limit are designated within tiers 1 through 4, and thusare preferred to analysts for whom the vehicle loan will exceed theanalyst fair allocation limit.

At step 6030, if the vehicle loan complexity exceeds the complexitylimit for the analyst, then the analyst will be considered in a highertier (e.g., tier 3 as opposed to tier 1, or tier 7 as opposed to tier 5)and less preferable. For example, based on table 5800, the simple,$20,000 vehicle loan would be within the complexity limits for allanalysts. However, if the $20,000 vehicle loan was “complex,” then itmay exceed the complexity limits for an analyst with a $35,000authority, the lowest authority available. Thus, in this case, a $35,000authority analyst may be considered to be a higher tier analyst for thecomplex loan in comparison to a $75,000 analyst.

At step 6040, unavailable analysts are considered a higher tier analystin comparison to the corresponding available analysts. Thus, if bothanalysts have an authority of $35,000, but one analyst is availablewhile the other analyst is unavailable, the unavailable analysts willhave a higher tier (i.e., tier 2) than the available analyst (i.e., tier1). Thus, tiered environment 6000 is another embodiment of an efficientmanner in which vehicle loans can be allocated to different analysts. Insome embodiments, the environment includes more, less, and/or differenttiers than those shown. Also, in some embodiments, the environmentdetermines tiers based on more, less, and/or different factors thanthose shown.

CONCLUSION

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium) or hardware. In hardware, the routines,etc., are tangible units capable of performing certain operations andmay be configured or arranged in a certain manner. In exampleembodiments, one or more computer systems (e.g., a standalone, client orserver computer system) or one or more hardware modules of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

What is claimed is:
 1. A computer implemented method for generatingprequalification vehicle loan offers for one or more applicantscomprising: receiving, via a graphical user interface, a vehicle loanapplication including vehicle loan information from an applicant;requesting credit data associated with the applicant from one or morecredit bureaus; receiving the credit data associated with the applicantfrom the credit bureaus, the credit data including a set of attributesfor the applicant; applying, by the one or more computer processors, afront end criteria to the applicant to determine whether the applicantis eligible for prequalification; and when the applicant is eligible forprequalification: determining, by the one or more computer processors,an estimated vehicle collateral value for a vehicle for the applicantbased on the set of attributes and/or vehicle loan information for theapplicant, determining, by the one or more computer processors, amaximum prequalified vehicle loan amount for a vehicle loan for thevehicle for the applicant based on the set of attributes and/or vehicleloan information for the applicant, generating, by the one or morecomputer processors, a prequalification offer for the applicant byincluding the estimated vehicle collateral value and the maximumprequalified vehicle loan amount, and causing, by the one or moreprocessors, the prequalification offer to be presented to the applicantvia the graphical user interface, the graphical user interfaceincluding: a first slider, coupled to an offer customization model,enabling the applicant to customize a first loan parameter of theprequalification offer, wherein a position of the first slider is afirst input into the offer customization model; a second slider, coupledto the offer customization model, enabling the applicant to customize asecond loan parameter of the prequalification offer, wherein a positionof the second slider is a second input into the offer customizationmodel; a region in each of the first and second sliders indicatingcompliant loan parameters, the region being dynamically determined bythe offer customization model based on the positions of the first andsecond sliders; a dynamic display of a customized loan offer, thecustomized loan offer being automatically updated by the offercustomization model as the user interacts with the first and secondsliders; a button that, when selected while the position of the firstand second sliders are in the respective regions indicating compliantloan parameters, enables the applicant to accept the customized loanoffer.
 2. The method of claim 1 wherein determining whether theapplicant is eligible for prequalification comprises verifying theapplicant has been automatically approved for vehicle loan underwriting.3. The method of claim 2, wherein applying the front end criteriacomprises excluding the applicant if the applicant has a FICO score lessthan or equal to
 680. 4. The method of claim 1, wherein receiving thevehicle loan application comprises receiving vehicle loan informationindicating a vehicle type, a vehicle condition, and a purchase type,wherein receiving credit data including a set of attributes for theapplicant comprises receiving at least a FICO score, a monthly income,and a monthly debt payment as part of the set of attributes for theapplicant, and wherein determining the maximum prequalified vehicle loanamount comprises determining the amount based off at least the vehicletype, the vehicle condition, the purchase type, the FICO score, themonthly income, and the monthly debt payment.
 5. The method of claim 1,wherein determining the estimated vehicle collateral value comprises:analyzing the set of attributes of the applicant to determine if thevehicle is premium collateral, high collateral, or low collateral;determining at least a premium collateral response probability based onthe set of attributes and/or vehicle loan information of the applicant;categorizing the vehicle as premium collateral, high collateral, or lowcollateral based on one or more determined collateral responseprobabilities; and assigning the vehicle an estimated vehicle collateralvalue based on how the vehicle was categorized.
 6. The method of claim5, wherein determining the maximum prequalified vehicle loan amountfurther comprises multiplying the estimated vehicle collateral value bya maximum loan to value (LTV) cutoff ratio allowed for the applicant togenerate the maximum prequalified vehicle loan amount, wherein themaximum LTV cutoff ratio defines the maximum value allowed when dividingthe maximum prequalified vehicle loan amount by the estimated vehiclecollateral value.
 7. The method of claim 5, wherein determining themaximum prequalified vehicle loan amount for the applicant furthercomprises; calculating a present value of one or more vehicle loanmonthly payments occurring for a vehicle loan term at a vehicle loanmonthly interest rate, wherein the vehicle loan monthly payment equals amaximum monthly vehicle loan payment that the applicant is allowed tomake towards a vehicle loan, wherein the vehicle loan term equals amaximum length allowed for the vehicle loan; and assigning the maximumprequalified vehicle loan amount a value equal to the present value. 8.The method of claim 7, wherein determining the maximum prequalifiedvehicle loan amount further comprises updating the maximum prequalifiedvehicle loan amount if a result of multiplying the estimated vehiclecollateral value by a maximum loan to value (LTV) cutoff ratio allowedfor the applicant is less than the present value, wherein the maximumLTV cutoff ratio defines the maximum value allowed when dividing themaximum prequalified vehicle loan amount by the estimated vehiclecollateral value.
 9. A computer system for generating prequalificationvehicle loan offers for one or more applicants comprising: one or morecomputer processors; and a program memory storing executableinstructions that when executed by the one or more computer processorscause the computer system to: receive, via a graphical user interface, avehicle loan application including vehicle loan information from anapplicant; request credit data associated with the applicant from one ormore credit bureaus; receive the credit data associated with theapplicant from the credit bureaus, the credit data including a set ofattributes for the applicant; apply, with the one or more computerprocessors, a front end criteria to the applicant to determine whetherthe applicant is eligible for prequalification; and when the applicantis eligible for prequalification: determine, with the one or morecomputer processors, an estimated vehicle collateral value for a vehiclefor the applicant based on the set of attributes and/or vehicle loaninformation for the applicant, determine, with the one or more computerprocessors, a maximum prequalified vehicle loan amount for a vehicleloan for the vehicle for the applicant based on the set of attributesand/or vehicle loan information for the applicant, generate, with theone or more computer processors, a prequalification offer for theapplicant by including the estimated vehicle collateral value and themaximum prequalified vehicle loan amount, and cause, with the one ormore computer processors, the prequalification offer to be presented tothe applicant via the graphical user interface, the graphical userinterface including: a first slider, coupled to an offer customizationmodel, enabling the applicant to customize a first loan parameter of theprequalification offer, wherein a position of the first slider is afirst input into the offer customization model; a second slider, coupledto the offer customization model, enabling the applicant to customize asecond loan parameter of the prequalification offer, wherein a positionof the second slider is a second input into the offer customizationmodel; a region in each of the first and second sliders indicatingcompliant loan parameters, the region being dynamically determined bythe offer customization model based on the positions of the first andsecond sliders; a dynamic display of a customized loan offer, thecustomized loan offer being automatically updated by the offercustomization model as the user interacts with the first and secondsliders; a button that, when selected while the position of the firstand second sliders are in the respective regions indicating compliantloan parameters, enables the applicant to accept the customized loanoffer.
 10. The system of claim 9 wherein the applicant has beenautomatically approved for vehicle loan underwriting.
 11. The system ofclaim 10, wherein the front end criteria includes excluding theapplicant if the applicant has a FICO score less than or equal to 680.12. The system of claim 9, wherein the vehicle loan informationindicates a vehicle type, a vehicle condition, and a purchase type,wherein the set of attributes for the applicant includes at least a FICOscore, a monthly income, and a monthly debt payment, and wherein themaximum prequalified vehicle loan amount is determined based off atleast the vehicle type, the vehicle condition, the purchase type, theFICO score, the monthly income, and the monthly debt payment.
 13. Thesystem of claim 9, wherein the instructions causing the system todetermine the estimated vehicle collateral value comprise instructionswhich further cause the system to: analyze the set of attributes of theapplicant to determine if the vehicle is premium collateral, highcollateral, or low collateral; determine at least a premium collateralresponse probability based on the set of attributes and/or vehicle loaninformation of the applicant; categorize the vehicle as premiumcollateral, high collateral, or low collateral based on one or moredetermined collateral response probabilities; and assign the vehicle anestimated vehicle collateral value based on how the vehicle wascategorized.
 14. The system of claim 13, wherein the maximumprequalified vehicle loan amount equals the result of multiplying theestimated vehicle collateral value by a maximum loan to value (LTV)cutoff ratio allowed for the applicant, wherein the maximum LTV cutoffratio defines the maximum value allowed when dividing the maximumprequalified vehicle loan amount by the estimated vehicle collateralvalue.
 15. The system of claim 13, wherein the instructions causing thesystem to determine the maximum prequalified vehicle loan amount for theapplicant further cause the system to; calculate a present value of oneor more vehicle loan monthly payments occurring for a vehicle loan termat a vehicle loan monthly interest rate, wherein the vehicle loanmonthly payment equals a maximum monthly vehicle loan payment that theapplicant is allowed to make towards a vehicle loan, wherein the vehicleloan term equals a maximum length allowed for the vehicle loan; andassign the maximum prequalified vehicle loan amount a value equal to thepresent value.
 16. The system of claim 15, wherein the maximumprequalified vehicle loan amount is updated if a result of multiplyingthe estimated vehicle collateral value by a maximum loan to value (LTV)cutoff ratio allowed for the applicant is less than the present value,wherein the maximum LTV cutoff ratio defines the maximum value allowedwhen dividing the maximum prequalified vehicle loan amount by theestimated vehicle collateral value.
 17. A non-transitorycomputer-readable storage medium comprising computer-readableinstructions to be executed on one or more processors of a system forgenerating prequalification vehicle loan offers for one or moreapplicants, the instructions when executed causing the one or moreprocessors to: receive, via a graphical user interface, a vehicle loanapplication including vehicle loan information from an applicant;request credit data associated with the applicant from one or morecredit bureaus; receive the credit data associated with the applicantfrom the credit bureaus, the credit data including a set of attributesfor the applicant; apply, with the one or more computer processors, afront end criteria to the applicant to determine whether the applicantis eligible for prequalification; and when the applicant is eligible forprequalification: determine, with the one or more computer processors,an estimated vehicle collateral value for a vehicle for the applicantbased on the set of attributes and/or vehicle loan information for theapplicant, determine, with the one or more computer processors, amaximum prequalified vehicle loan amount for a vehicle loan for thevehicle for the applicant based on the set of attributes and/or vehicleloan information for the applicant, generate, with the one or morecomputer processors, a prequalification offer for the applicant byincluding the estimated vehicle collateral value and the maximumprequalified vehicle loan amount, and cause, with the one or morecomputer processors, the prequalification offer to be presented to theapplicant via the graphical user interface, the graphical user interfaceincluding: a first slider, coupled to an offer customization model,enabling the applicant to customize a first loan parameter of theprequalification offer, wherein a position of the first slider is afirst input into the offer customization model; a second slider, coupledto the offer customization model, enabling the applicant to customize asecond loan parameter of the prequalification offer, wherein a positionof the second slider is a second input into the offer customizationmodel; a region in each of the first and second sliders indicatingcompliant loan parameters, the region being dynamically determined bythe offer customization model based on the positions of the first andsecond sliders; a dynamic display of a customized loan offer, thecustomized loan offer being automatically updated by the offercustomization model as the user interacts with the first and secondsliders; a button that, when selected while the position of the firstand second sliders are in the respective regions indicating compliantloan parameters, enables the applicant to accept the customized loanoffer.
 18. The non-transitory computer-readable storage medium of claim17, wherein instructions causing the one or more computer processors todetermine the estimated vehicle collateral value comprise instructionswhich further cause the one or more processors to: analyze the set ofattributes of the applicant to determine if the vehicle is premiumcollateral, high collateral, or low collateral; determine at least apremium collateral response probability based on the set of attributesand/or vehicle loan information of the applicant; categorize the vehicleas premium collateral, high collateral, or low collateral based on oneor more determined collateral response probabilities; and assign thevehicle an estimated vehicle collateral value based on how the vehiclewas categorized.
 19. A computing device configured to present agraphical user interface for dynamically customizing a loan offer, thegraphical user interface being presented subsequent to determining thatan applicant associated with the customized loan offer is eligible forprequalification based on front end criteria, the graphical userinterface comprising: a first slider coupled to an offer customizationmodel and corresponding to a first custom input value, wherein the offercustomization model dynamically utilizes the first custom input value asan input; a second slider coupled to the offer customization model andcorresponding to a second custom input value, wherein the offercustomization model dynamically utilizes the second custom input valueas an input; a region on the first slider corresponding to compliantfirst custom input values as dynamically determined by the offercustomization model based on the first and second custom input values; aregion on the second slider corresponding to compliant second custominput values as dynamically determined by the offer customization modelbased on the first and second custom input values; an indication of acustomized loan offer dynamically determined by the offer customizationmodel based upon at least the first custom input value, the secondcustom input value, and at least one other loan parameter; and a buttonthat, when selected while the first and second custom input values arecompliant, enables the user to accept the customized loan offer.